The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index; none of the 4 sources its claims cite is checked for support yet.
Learning from Demonstrations Through the Use of Non-rigid Registration
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper presents trajectory transfer, a method that adapts a demonstrated end-effector trajectory to a new scene by non-rigidly registering the training point cloud to the test point cloud and warping the trajectory accordingly. It validates the approach mainly by enabling a PR2 robot to tie five knot types from demonstrations, and also shows qualitative results on other manipulation tasks.
Key findings
- With one demonstration per knot, success under random rope perturbations depended on perturbation size: at \(d_{pert}=3\) cm, success was 5/5 for overhand, figure-eight, and square, 3/5 for double-overhand, and 1/5 for clove hitch; at \(d_{pert}=10\) cm, success was 4/5, 1/5, 3/5, 3/5, and 0/5 respectively.
- The PR2 autonomously tied five knot types: overhand, figure-eight, double-overhand, square knot, and clove hitch around a pole.
- For the overhand knot, the authors collected 148 trajectory segments, including 36 demonstrations of the standard 3-step sequence and 40 additional segments starting from failure states; the nearest-neighbor policy was qualitatively able to tie knots from a larger set of initial configurations and recover from failure states.
- The same algorithm and code were applied to folding a T-shirt, picking up a plate with a two-arm motion, and opening a bottle, with qualitative success reported.
Methods
- Trajectory transfer: register the training scene point cloud to the test scene point cloud using a modified TPS-RPM non-rigid registration algorithm, then apply the resulting transformation \(f\) to demonstrated end-effector positions and use the Jacobian \(Jf(p)\) to transform orientations.
- Convert the transferred end-effector trajectory into a joint trajectory by solving an optimization problem that trades off smoothness, end-effector pose error, collision avoidance, and joint limits, using the trajectory optimization method from Schulman et al. and initializing from the demonstration.
- For multi-step tasks, repeatedly choose the nearest demonstration by registration cost, apply trajectory transfer, and execute the resulting trajectory; experiments used a PR2 with kinesthetic teaching and RGBD point clouds, with rope points extracted by color filtering.
Limitations
- The paper states that the cost-function invariance assumption in Equation (7) is strong and can fail even for rigid transformations, for example when absolute end-effector orientation matters.
- The paper states that dynamics covariance holds only approximately in knot tying and was checked qualitatively by comparing final rope states at training and test time.
- The paper reports only qualitative results for the many-demonstration overhand knot policy and for the other manipulation tasks; no quantitative success rates are given for those settings. The single-demonstration results also show low success for clove hitch and for larger perturbations.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
13 Own result · 12 Own interpretation · 3 No full text of the sources · 1 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As our main experimental validation, we enable a PR2 robot to autonomously tie five different types of knots in rope.”
- Also stated in: § 5 Experiments with rope · p. 9 · plain
“We enabled the PR2 to autonomously tie five different types of knot: overhand, figure-eight, double-overhand, square knot, and clove hitch (around a pole).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 5 Experiments with rope · p. 9 · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This policy recovers from failures, assuming that the demonstrator has provided examples of the failure states along with the corrective motions that get out of them.”
- Also stated in: § 4 Generalizing from many examples: nearest neighbor method · p. 9 · plain
“This policy enables the robot to recover from errors, provided that the demonstrator has provided a demonstration starting from the failure state.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The demonstrator has provided examples of the failure states along with the corrective motions that get out of them.
- Also stated in: § 4 Generalizing from many examples: nearest neighbor method · p. 9 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and it is also able to choose corrective movements to recover from the failure states.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that the regularization r ( B ) was necessary because sometimes the transformation is underdetermined in certain dimensions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Bottom row, left: demonstration and autonomous execution of plate pickup. Round plate from training was registered to rectangular plate at test time.”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we find that the nearest-neighbor policy from Section 4 is able to successfully tie knots from a much larger set of initial configurations than was possible with a single demonstration”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two of the common failure modes were (1) the rope would end up in a previously unseen crossing state, often due to a small difference in its position; (2) the robot would grab the wrong piece of rope, or two pieces instead of one.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our experiments, this initialization strategy led to finding good locally-optimal trajectories.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“executing shirt folding procedure autonomously on small T-shirt, based on single demonstration on large T-shirt.”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As expected, the failure rate increases at higher d pert, where the test situation is further away from the training situation.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 5
- The quote only partly states this claim. 1
- Probably not a substantial claim. 4
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Two of the common failure modes were (1) the rope would end up in a previously unseen crossing state, often due to a small difference in its position; (2) the robot would grab the wrong piece of rope, or two pieces instead of one.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Bottom row, right: demonstration and autonomous execution of bottle opening. The bottle used at test time had a different size and shape from the one used for training.”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Assumption 4 holds approximately in this domain; we have checked this qualitatively by comparing the final states of the rope at training and test time.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that this approach was not sufficient because there is often no continuous and collision-free trajectory that achieves the desired end-effector trajectory, so we need to compromise.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With this quadratic regularization term, the optimization problem still can be solved analytically as a least squares problem.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That said, one can imagine situations where Equation (7) does not hold, even for a rigid transformation f -for example, when absolute orientation of the robot's end-effector matters.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As shown in [18], Equation (1) can be analytically solved efficiently; details are provided in Appendix 9.1.”
What the sources say
- Not checked — no full text of the sourcecites [18] Wahba, G.: Spline models for observational data, Society for Industrial Mathematics, vol. 59 (1990) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The above results suggest that when starting from a state that is very close to a demonstration, the success rate is high, at least for the easier knots.”
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the knot tying domain, the goal set is defined topologically, and a transformation f will preserve it, provided that it is a homeomorphism (i.e., a continuous function whose inverse is continuous), justifying assumption 3.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, our approach operates directly on point clouds-the outputs of our sensor hardware-so it is suitable for this task, and it can be applied to a new task without developing a new vision system.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
The demonstrator has provided examples of the failure states along with the corrective motions that get out of them.
“This policy recovers from failures, assuming that the demonstrator has provided examples of the failure states along with the corrective motions that get out of them.”
The authors state this as an assumption.
There exists a class of smooth transformations f such that L(STATE, TRAJ) = L(f(STATE), f(TRAJ)).
“Our trajectory transfer procedure can be justified by assuming that there is a class of smooth transformations f with the following property:”
The authors state this as an assumption.
The dynamics of the system are covariant under the transformation f (Equation 10 holds).
“4. Equation (10) holds, i.e., the dynamics are covariant under f .”
The authors state this as an assumption.
The goal set is preserved by f (f(G)=G).
“3. f ( G ) = G , i.e., the goal set is preserved by f”
The authors state this as an assumption.
f transforms STATE_train into STATE_test (f(STATE_train)=STATE_test).
“1. f ( STATE t train ) = STATE t test , i.e., f transforms STATE train into STATEtest.”
The authors state this as an assumption.
The demonstration ends up in the goal state (STATE_{t+1}^{train} ∈ G).
“2. STATE t + 1 train ∈ G , i.e., the demonstration ends up in the goal state.”
The paper relies on this without support.
For each segment, the end-effector trajectory depends on the geometry of the rope in some way that must be inferred by the algorithm.
“For each of these segments, the end-effector trajectory ought to depend on the geometry of the rope in some way that must be inferred by the algorithm.”
The paper relies on this without support.
The procedure for tying a given type of knot can be broken into several segments based on grab and release events.
“The procedure for tying a given type of knot can be broken into several segments based on grab and release events.”
Citations
Cites
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.2307/2938687 · Cited by 7,398
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994
3D is here: Point Cloud Library (PCL)
Rusu, R., & Cousins, S. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 5,513
Spline Models for Observational Data.
Müller, H., & Wahba, G. (1991) · Journal of the American Statistical Association · doi:10.2307/2290434 · Cited by 4,992
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
Abraham, R., & Young, H. D. (1968) · American Journal of Physics · doi:10.1119/1.1974504 · Cited by 3,129
Image quilting for texture synthesis and transfer
Efros, A. A., & Freeman, W. T. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383296 · Cited by 2,435
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
(2008) · doi:10.1109/iros13647.2008 · Cited by 2,203
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“Thus, transfer learning may also be of crucial importance (Schulman et al., 2016; Liu et al., 2017).”
Recent Advances in Robot Learning from Demonstration
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“It has been applied successfully to a wide range of domains in robotics, for example to autonomous driving [4], [5], [6], autonomous helicopter flight [7], gesturing [8], and manipulation [9], [10].”
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“[1] for warping the demonstrated trajectories using the TPS warping function.”
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BackgroundToward next-generation learned robot manipulation
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“include invariance of certain state variables [10, 11], allowed variance of certain state variables [5, 6], characteristics of the task in different parts of the state space [15, 8], relativeness to objects in the environment [12], segmentations of the task [12], task constraints [14] or partial-ordering of task steps…”
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“ng to design and not generally available o-the-shelf for robotics tasks. Additional fabric manipulation techniques include trajectory transfer and learning from image-based wrinkles. Schulman et al. [27] propose to adapt a demonstrated trajectory (including pick points) from training time to the geometry at test time…”
BackgroundTransition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning
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“The loss function for the RNN is a simple Behavioral Cloning loss L θ ( a t : t + T , s t : t + T .”
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“trained robots to tie knots using human demonstration via trajectory tra sferring [5].”
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“dynamic models, can be further grouped into two types: policy learning [15]–[17] and trajectory learning [18]–[20].”
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BackgroundLearning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Ganapathi, A., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Seita, D., Grannen, J., Hwang, M., Hoque, R., Gonzalez, J. E., Jamali, N., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561980 · Cited by 69
“While there is significant prior work on geometric [1, 23, 35, 46] and learning based approaches [36, 37, 47] for fabric manipulation, these approaches often involve designing or learning task-specific manipulation policies, making it difficult to efficiently reuse information across tasks.”
BackgroundAutomated planning for robotic layup of composite prepreg
Malhan, R. K., Shembekar, A. V., Kabir, A. M., Bhatt, P. M., Shah, B. C., Zanio, S., Nutt, S., & Gupta, S. K. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102020 · Cited by 58
Movement primitives via optimization
Dragan, A. D., Muelling, K., Bagnell, J. A., & Srinivasa, S. S. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139510 · Cited by 52
“Among several tools for addressing this problem [1, 4–6], a commonly used one is a Dynamic Movement Primitive (DMP) [7, 8].”
MethodAn online method for tight-tolerance insertion tasks for string and rope
Wang, W., Berenson, D., & Balkcom, D. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139532 · Cited by 51
“We use a quasi-static model, so that the the equilibrium configuration of the flexible object is completely determined by the configuration(s) of the grippers (represented by a vector q) together with internal spring and external gravity forces.”
MethodDeep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
Zhang, T., McCarthy, Z., Jow, O., Lee, D., Chen, X., Goldberg, K., & Abbeel, P. (2017) · arXiv · doi:10.48550/arxiv.1710.04615 · Cited by 618
“It has been applied successfully to a wide range of domains in robotics, for example to autonomous driving [4], [5], [6], autonomous helicopter flight [7], gesturing [8], and manipulation [9], [10].”
Self-citationOnline Trajectory Planning in Dynamic Environments for Surgical Task Automation
Osa, T., Sugita, N., & Mitsuishi, M. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.011 · Cited by 48
Mandlekar, A., Booher, J., Spero, M., Tung, A., Gupta, A., Zhu, Y., Garg, A., Savarese, S., & Fei-Fei, L. (2019) · arXiv · doi:10.48550/arxiv.1911.04052 · Cited by 48
Cloth Manipulation Using Random-Forest-Based Imitation Learning
Jia, B., Pan, Z., Hu, Z., Pan, J., & Manocha, D. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2897370 · Cited by 42
“Recently, [31, 32, 33] proposed using non-rigid registration to transfer human demonstrations of cloth manipulations to real robots and [34] required an adaptive cloth simulator to predict the future state of a cloth.”
BackgroundMotion Planning for Dual-Arm Manipulation of Elastic Rods
Sintov, A., Macenski, S., Borum, A. D., & Bretl, T. W. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3011352 · Cited by 45
“surgical suturing [2], knot tying [3], and automated handling of flexible material such as sheet metal parts [4].”
BackgroundLet’s Take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation
Gregorio, D. D., Palli, G., & Stefano, L. D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_42 · Cited by 42
Robust Deformation Model Approximation for Robotic Cable Manipulation
Jin, S., Wang, C., & Tomizuka, M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968157 · Cited by 42
“[10] proposed to teach robots to manipulate deformable objects from human demonstrations.”
BackgroundIterative Residual Policy for Goal-Conditioned Dynamic Manipulation of Deformable Objects
Chi, C., Burchfiel, B., Cousineau, E. A., Feng, S., & Song, S. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.016 · Cited by 43
Mandlekar, A., Booher, J., Spero, M., Tung, A. T. Y., Gupta, A., Zhu, Y., Garg, A., Savarese, S., & Fei-Fei, L. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968114 · Cited by 60
Huang, S. H., Pan, J., Mulcaire, G., & Abbeel, P. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353475 · Cited by 39
Self-citationRobots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
Zhang, H., Ichnowski, J., Seita, D., Wang, J., Huang, H., & Goldberg, K. Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561630 · Cited by 40
“Various approaches have been proposed for quasistatic manipulation of cables, such as using demonstrations [16], [17], [18], self-supervised learning [19], [20], model-free reinforcement learning [21], [22], contrastive learning [23], dense object descriptors [24], generative modeling [25], and state estimation [26].”
MethodLee, A. X., Gupta, A., Lu, H. H., Levine, S., & Abbeel, P. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354120 · Cited by 34
“Thin Plate Splines As in the previous work that uses non-rigid registration for trajectory transfer [1], we use the thin plate spline (TPS) [18], [19] parametrization for the transformation T , which can be obtained by regularizing the second-order derivatives of the transformation.”
BackgroundMethodInfluentialSelf-citationOn the manipulation of articulated objects in human-robot cooperation scenarios
Capitanelli, A., Marco Maratea, A., Mastrogiovanni, F., & Vallati, M. (2018) · Archive of Doctoral Theses and Digital Collections (University of Calabria) · Cited by 34
Lee, A. X., Huang, S. H., Hadfield-Menell, D., Tzeng, E., & Abbeel, P. (2014) · 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2014.6943185 · Cited by 32
“’s approach to learning from demonstrations [1] calculates the new trajectory by first using a modified thin plate spline robust point matching (TPS-RPM) algorithm [19] to calculate a non-rigid scene registration that maps points from the demonstration scene to the new scene.”
BackgroundMethodResultInfluentialSelf-citationOsa, T., Harada, K., Sugita, N., & Mitsuishi, M. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907819 · Cited by 34
“proposed a related method [5], [8] that allows the generalization of demonstrated trajectories to new situations by computing a mapping from a demonstration situation onto a new situation.”
MethodGeneralizing pouring actions between objects using warped parameters
Brandi, S., Kroemer, O., & Peters, J. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041426 · Cited by 34
“[7] computed warping functions for the entire scene.”
BackgroundLearning legged swimming gaits from experience
Meger, D., Higuera, J. C. G., Xu, A., et al. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139509 · Cited by 35
“This choice is inspired by authors such as Xu et al. [15], [16] who have recently shown that human guidance, in the forms of demonstrations or direct participation, are effective aids for learning practical robotic tasks.”
Surgical GAN: Towards real-time path planning for passive flexible tools in endovascular surgeries
Zhao, Y. H., Wang, Y., Zhang, J., Liu, X., Li, Y., Guo, S., Xu, Y., & Hong, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.044 · Cited by 33
Estimating Model Utility for Deformable Object Manipulation Using Multiarmed Bandit Methods
McConachie, D., & Berenson, D. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2822669 · Cited by 29
“Approaches like [14] and [15] bypass this challenge by using off-line demonstrations to teach the robot specific manipulation tasks; however, when a new task is attempted, a new training set needs to be generated.”
BackgroundBandit-Based Model Selection for Deformable Object Manipulation
McConachie, D., & Berenson, D. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_45 · Cited by 25
“Approaches like [6,7] bypass this challenge by using offline demonstrations to teach the robot specific manipulation tasks; however, when a new task is attempted a new training set needs to be generated.”
BackgroundKadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27
Learning Visual Affordance Grounding From Demonstration Videos
Luo, H., Zhai, W., Zhang, J., Cao, Y. H., & Tao, D. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3298638 · Cited by 33
“Schulman et al. [29] proposed a method based on nonrigid trajectory transfer for adapting the demonstrated trajectory from the training geometry to the test geometry, enabling the robot to tie different types of knots in the rope automatically.”
MethodRobotic manipulation of deformable objects by tangent space mapping and non-rigid registration
Tang, T., Liu, C., Chen, W., & Tomizuka, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759418 · Cited by 25
“[1] proposed a non-rigid registration method to teach the robot to manipulate the deformable objects by human demonstration.”
MethodDynamically Consistent Online Adaptation of Fast Motions for Robotic Manipulators
Pekarovskiy, A., Nierhoff, T., Hirche, S., & BUSS, M. J. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2765666 · Cited by 24
“A nonrigid registration [23] method is applied to the tasks with variable scene geometry and initial conditions.”
MethodKnot-tying with flying machines for aerial construction
Augugliaro, F., Zarfati, E., Mirjan, A., et al. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354218 · Cited by 24
“While rope manipulation and knot-tying have been extensively studied in the context of dexterous manipulation (see, for example, [19] and reference therein), this work approaches knot-tying by taking into account the specific characteristics of flying machines.”
BackgroundUsing dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot
Liang, J., Mahler, J., Laskey, M., Li, P., & Goldberg, K. Y. (2017) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2017.8256067 · Cited by 22
“Schulman et al. [39] used Thin Plate Splines to warp kinesthetically demonstrated trajectories of rope tying with a PR2 to solve novel test cases.”
MethodLearning generalizable surface cleaning actions from demonstration
Elliott, S., Xu, Z., & Çakmak, M. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172424 · Cited by 23
“dressed challenges in manipulating objects [20], [3], learning high-level task structures [16], [24], and learning from nonexpert demonstrators [8], [26].”
BackgroundA Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
Bajracharya, M., Borders, J., Helmick, D., Kollar, T., Laskey, M., Leichty, J., Ma, J., Nagarajan, U., Ochiai, A., Petersen, J., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196677 · Cited by 24
Face valuing: Training user interfaces with facial expressions and reinforcement learningPreprint
Veeriah, V., Pilarski, P. M., & Sutton, R. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02807 · Cited by 21
“There are numerous works exploring learning from demonstration (e.g., Koenig & Mataric, 2012; Schulman et al., 2013; Alizadeh et al., 2014).”
BackgroundOne-shot robot programming by demonstration using an online oriented particles simulation
Groth, C., & Henrich, D. (2014) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2014.7090323 · Cited by 20
“The approach in [18] uses a so called non-rigid registration to transfer”
MethodSakr, M., Freeman, M., der Loos, H. F. M. V., & Croft, E. A. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223430 · Cited by 21
“Others have used kinesthetic teaching in more complex tasks such as ball paddling [11], box flipping using chopsticks [12] and knot tying [13].”
MethodOnline, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Islam, F., Salzman, O., & Likhachev, M. A. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/683 · Cited by 19
“While there has been much work on planning using human demonstration (see, e.g., [2], [14], [25], [28], [35]), there has been far less research incorporating guidance as an interactive part of the planning loop.”
BackgroundGanapathi, A., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Seita, D., Grannen, J., Hwang, M., Hoque, R., Gonzalez, J. E., Jamali, N., et al. (2020) · Cited by 19
Real-Time Non-Visual Shape Estimation and Robotic Dual-Arm Manipulation Control of an Elastic Wire
Mishani, I., & Sintov, A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3128707 · Cited by 19
“Other applications include knot tying [5], surgical suturing [6], hot wire carving recently demonstrated by a robotic system [7] or manipulation of cables using autonomous aerial vehicles [8].”
BackgroundLocal Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
Chun, E., Du, Y., Simeonov, A., et al. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160423 · Cited by 18
“When object models are known, pose estimation may be used for manipulation [22, 30, 31].”
MethodTrajectory-Based Skill Learning Using Generalized Cylinders
Ahmadzadeh, S. R., & Chernova, S. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00132 · Cited by 18
Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations
Kang, J. H., Joshi, S., Huang, R., & Gupta, S. K. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3553689 · Cited by 26
“It has demonstrated remarkable potential across various real-world robotic manipulation tasks [9], [10], [11], [12], [13], [14], [15].”
Iterative residual policy: For goal-conditioned dynamic manipulation of deformable objects
Chi, C., Burchfiel, B., Cousineau, E. A., Feng, S., & Song, S. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201201 · Cited by 17
“Goal-conditioned manipulation is particularly difficult for deformable objects due to their high degree-of-freedom and under-actuation [1, 21, 18, 22, 25, 26, 9, 24].”
BackgroundInterleaving Planning and Control for Deformable Object Manipulation
McConachie, D., Ruan, M., & Berenson, D. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-28619-4_68 · Cited by 15
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth DataPreprint
Sundaresan, P., Grannen, J., Thananjeyan, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01835 · Cited by 16
“There is also extensive literature on deformable manipulation [18, 25, 27, 36, 40].”
BackgroundRobotic Actuation and Control of a Catheter for Structural Intervention Cardiology
Zhang, X., Palumbo, M. C., Perico, F., Magro, M., Fortuna, A., Magni, T., Votta, E., Segato, A., & Momi, E. D. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981676 · Cited by 17
“This enabled the robot to perform the task on its own, even under different conditions [12].”
MethodFeedback motion planning for liquid pouring using supervised learning
Pan, Z., & Manocha, D. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202300 · Cited by 15
“The extension of conventional motion planning algorithms to the manipulation of non-rigid objects has been addressed in the context of virtual suturing [21], cloth folding [22], and surgical simulation [23].”
BackgroundLearning Dense Visual Correspondences in Simulation to Smooth and Fold Real FabricsPreprint
Ganapathi, A., Sundaresan, P., Thananjeyan, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12698 · Cited by 15
“[11, 12] show impressive human-robot fabric manipulation under the assumption that fabric has already been grasped, and Schulman et al. [33] demonstrate deformable object manipulation while requiring task-specific kinesthetic demonstrations.”
Multi-Stage Suture Detection for Robot Assisted Anastomosis Based on Deep Learning
Hu, Y., Gu, Y., Yang, J., & Yang, G. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461131 · Cited by 15
“perception to obtain 3D points cloud of the thread, probabilistic model [4], deformable registration [5] and graph model [6] have been applied to these recognition processes.”
MethodLee, A. X., Goldstein, M. A., Barratt, S. T., & Abbeel, P. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139289 · Cited by 12
“Abstract— Recent work [1], [2], [3] has shown promising results in learning from demonstrations for the manipulation of deformable objects.”
BackgroundInfluentialSelf-citationSimultaneous End-User Programming of Goals and Actions for Robotic Shelf Organization
Liang, Y., Pellier, D., Fiorino, H., et al. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593518 · Cited by 12
“A majority of the work focuses on directly learning a policy or modeling higher level actions from lower level control signals [11], [12], [13], [14], while some explore learning task goals or task structure represented in various ways [15], [16], [17], [18], [19].”
BackgroundA novel cable-grasping planner for manipulator based on the operation surface
Wu, W. H., Zhu, Y., Zheng, X., & Guo, Y. H. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102252 · Cited by 11
Generalized Cylinders for Learning, Reproduction, Generalization, and Refinement of Robot Skills
Ahmadzadeh, S. R., Rana, M. A., & Chernova, S. (2017) · doi:10.15607/rss.2017.xiii.021 · Cited by 12
“[25] demonstrated the usefulness of nonrigid registration in LfD by employing it for autonomous knot tying.”
BackgroundAutomated Planning Techniques for Robot Manipulation Tasks Involving Articulated Objects
Capitanelli, A., Maratea, M., Mastrogiovanni, F., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70169-1_36 · Cited by 11
Seita, D., Florence, P., Tompson, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03385 · Cited by 11
“In recent years, learning-based approaches for manipulation have grown in popularity to improve the generalization of knot-tying and also to manipulate rope towards general target configurations [52], [46], [49], [57], [64], [61].”
BackgroundNIFT: Neural Interaction Field and Template for Object Manipulation
Huang, Z., Xu, J., Dai, S., et al. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160666 · Cited by 10
“For example, pose estimation [28, 35, 39] is the key for known objects, while primitive-based templatematching [13, 27, 34] can be more robust to shape and pose changes.”
BackgroundCross-Scene Suture Thread Parsing for Robot Assisted Anastomosis based on Joint Feature Learning
Gu, Y., Hu, Y., Zhang, L., Yang, J., & Yang, G. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593622 · Cited by 9
“The typical examples include rope detection [10], [11], [12], [13] and thread detection [4], [1], [2], [3], [14].”
BackgroundMethodEfficient programming of manipulation tasks by demonstration and adaptation
Elliott, S., Toris, R., & Çakmak, M. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172448 · Cited by 9
Computational Modeling: Human Dynamic Model
Liu, L., Cooper, J. L., & Ballard, D. H. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.723428 · Cited by 9
Shape-Based Transfer of Generic Skills
Thompson, S., Kaelbling, L. P., & Lozano‐Pérez, T. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560894 · Cited by 10
“Schulman et al. [5] and Lee et al. [6] describe a method for adapting task demonstrations across objects of varying geometry by performing non-rigid registration between a known scene and a novel one.”
MethodAutomated Planning Encodings for the Manipulation of Articulated Objects in 3D with Gravity
Bertolucci, R., Capitanelli, A., Maratea, M., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35166-3_10 · Cited by 9
Learning From Observation of Tabletop Knotting Using a Simple Task Model
Takizawa, M., Yao, Z., Onda, H., Kudoh, S., & Suehiro, T. (2019) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2019.8700429 · Cited by 9
“of a person, who moved to teach a trajectory that fitted the situation [15].”
BackgroundManipulation of Articulated Objects Using Dual-arm Robots via Answer Set Programming
Bertolucci, R., Capitanelli, A., Dodaro, C., et al. (2020) · Theory and Practice of Logic Programming · doi:10.1017/s1471068420000459 · Cited by 8
A Simulation-Based Grasp Planner for Enabling Robotic Grasping during Composite Sheet Layup
Manyar, O. M., Desai, J., Deogaonkar, N., Joesph, R. J., Malhan, R. K., McNulty, Z., Wang, B., Barbič, J., & Gupta, S. K. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560939 · Cited by 8
Control flow for robust one-shot robot programming using entity-based resources
Orendt, E. M., & Henrich, D. (2017) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2017.8023498 · Cited by 7
“Although there is just a small number of One-Shot PbD approaches, but they all match in the benefit of intuitive use [6], [5], [8], [9], [10].”
BackgroundOnline Grasp Plan Refinement for Reducing Defects During Robotic Layup of Composite Prepreg Sheets
Malhan, R. K., Joseph, R. J., Shembekar, A. V., Kabir, A. M., Bhatt, P. M., & Gupta, S. K. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196876 · Cited by 8
Optimal Cosserat-based deformation control for robotic manipulation of linear objects
Artinian, A., Huet, Q., Amar, F. B., & Perdereau, V. (2023) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim46323.2023.10196216 · Cited by 7
“These approaches have the advantage of not relying on a specific model but they are often hard to generalize from a small amount of training data [13], [14].”
BackgroundTowards Occlusion-Aware Pose Estimation of Surgical Suturing Threads
Gu, Y., Yang, J., & Yang, G. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3198402 · Cited by 6
“whether a predefined model is used, they can be categorized into explicit methods [3], [4], [5], [6], [7], [8], [9], [10], [11] and implicit methods [12], [13], [14], [15], [16], [17], [18], [19],”
MethodLearning-based Feedback Controller for Deformable Object ManipulationPreprint
Jia, B., Hu, Z., Pan, Z., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09618 · Cited by 6
“accomplish the task. The approaches in the final group do not explicitly model the deformation parameters of the object. Instead, they use vision or learning methods to accomplish tasks directly [1], [10], [14], [22]–[26]. These methods focus on high-level policies but cannot achieve accurate operations; their success…”
BackgroundInfluentialRobots of the Lost Arc: Learning to Dynamically Manipulate Fixed-Endpoint Ropes and Cables.Preprint
Zhang, H., Ichnowski, J., Seita, D., Wang, J., & Goldberg, K. Y. (2020) · arXiv (Cornell University) · Cited by 4
Wang, Z., & Qureshi, A. H. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3539910 · Cited by 6
“In recent years, learning-based methods [9], [10], [11], [12], [13], [14], [15], [16], [17] have grown rapidly and demonstrated impressive performance and generalization abilities.”
Perception of Deformable Objects and Compliant Manipulation for Service Robots
Stückler, J., & Behnke, S. (2015) · Soft Robotics · doi:10.1007/978-3-662-44506-8_7 · Cited by 5
“In contrast to the method in [8,9], we do not assume the estimated displacement field to be valid at any pose on the motion trajectory.”
BackgroundMethodBeyond lowest-warping cost action selection in trajectory transfer
Hadfield-Menell, D., Lee, A. X., Finn, C., et al. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139644 · Cited by 5
“Recent approaches leverage TPS to perform trajectory transfer [2].”
BackgroundMethodInfluentialSelf-citationDesign and implementation of a multi-DOF hand and basic motions for rope-knotting tasks
Takizawa, M., Kudoh, S., & Suehiro, T. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2070866 · Cited by 5
Movement primitives with multiple phase parameters
Ewerton, M., Maeda, G. J., Neumann, G. M., et al. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487134 · Cited by 5
“Learning from human demonstrations has been playing an increasingly important role in robotics, especially since robots are getting better at adapting demonstrated movements to new situations and demands [1], [2], [3].”
BackgroundTrajectory Adaptation of Robot Arms for Head-Pose Dependent Assistive Tasks.
Rahmatizadeh, R., Abolghasemi, P., Bölöni, L. L., Jabalameli, A., & Behal, A. (2016) · Journal of International Crisis and Risk Communication Research · Cited by 5
Metadata
- Authors
- Schulman, J., Ho, J., Lee, C., & Abbeel, P.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Schulman, John
- Venue
- Springer Tracts in Advanced Robotics
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 339-354
- ISSN
- 1610-742X (ISSN-L)
- 1610-7438
- OpenAlex
- W2472053385
- Cited by
- 133
- References
- 31
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 9 · 2022: 14 · 2021: 15 · 2020: 18 · 2019: 12 · 2018: 18 · 2017: 13 · 2016: 6
- FWCI
- 38.92
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer tracts in advanced robotics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- imitation learning, non-rigid registration, robotic manipulation, multi-step tasks, PR2 robot, knot tying, rope manipulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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