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.
Integrity signals
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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
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Cited by
Sorted by citations
Items 101–139 of 139
Visualizing Crocheting Overlay
Cho, S., Nguyen, L. L., Wen, Y., et al. (2022) · Creativity and Cognition · doi:10.1145/3527927.3533024 · Cited by 5
“, understanding the ends of the rope and its trajectory [18]).”
BackgroundUntangling Dense Knots by Learning Task-Relevant KeypointsPreprint
Grannen, J., Sundaresan, P., Thananjeyan, B., Ichnowski, J., Balakrishna, A., Hwang, M., Viswanath, V., Laskey, M., Gonzalez, J. E., & Goldberg, K. Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04999 · Cited by 30
“Other methods present algorithms for knot tying and cable manipulation but do not leverage geometric structure [9, 6, 2], whereas we reason about the geometry of intersections to infer appropriate untangling actions.”
BackgroundMethodInfluentialLearning Rhythmic Trajectories With Geometric Constraints for Laser-Based Skincare Procedures
Duan, A., Liuchen, W., Wu, J., Camoriano, R., Rosasco, L., & Navarro-Alarcón, D. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3543301 · Cited by 5
“Algorithm 1: Imitation of geometric trajectory with rhythm by structured prediction. toward new environments, the applications have been limited to deterministic scenarios [48].”
Study on Tying of a Deformable Band-shaped Object by a Dual Arm Robot
Seo, A., Takizawa, M., Kudoh, S., & Suehiro, T. (2019) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2019.8700456 · Cited by 4
“proposed a method to solve the problem caused by differences in the shapes of DLOs at the time of learning and execution by using non-rigid registration [4].”
MethodA Mobile Manipulation System for One-Shot Teaching of Complex Tasks in HomesPreprint
Bajracharya, M., Borders, J., Helmick, D., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00127 · Cited by 4
Onda, H., Kudoh, S., & Suehiro, T. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2518741 · Cited by 3
“Schulman et al. introduced the concept of trajectory transfer and dealt with the issue of the geometry of the relevant objects varying from one trial to another in teaching by demonstration [9].”
BackgroundAn ASP-Based Framework for the Manipulation of Articulated Objects Using Dual-Arm Robots
Bertolucci, R., Capitanelli, A., Dodaro, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20528-7_3 · Cited by 3
Programming by Demonstration with User-Specified Perceptual LandmarksPreprint
Huang, J. T., & Çakmak, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00565 · Cited by 3
“, ends of a rope to be tied [3], but these parts are detected with a special-purpose perception system that cannot be used for any other object.”
BackgroundInfluentialLearning a thousand tasks in a day
Dreczkowski, K., Vitiello, P., Vosylius, V., & Johns, E. (2025) · Science Robotics · doi:10.1126/scirobotics.adv7594 · Cited by 13
“Task: A verb plus a specific target object instance (e.g. “unzip the round pink handbag”)”
Development of a Whole-Body Work Imitation Learning System by a Biped and Bi-Armed Humanoid
Matsuura, Y., Kawaharazuka, K., Hiraoka, N., Kojima, K., Okada, K., & Inaba, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342502 · Cited by 4
Learning from Human Hand Demonstration for Wire Harness Grasping
Kamiya, K., Wang, Y., Jia-Xi, L., et al. (2024) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim55361.2024.10637203 · Cited by 2
Automating Deformable Gasket Assembly
Adebola, S., Sadjadpour, T., El-Refai, K., Panitch, W., Ma, Z., Lin, R., Qiu, T., Ganti, S., Le, C., Drake, J., et al. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711836 · Cited by 2
“A wide range of methods have been explored to solve different aspects of this problem, including Coherent Point Drift for DLO state estimation [14], learning from demonstrations for DLO manipulation [15–19], and self-supervised learning for understanding the state-space of DLOs [20].”
MethodLearning Manipulation of Deformable Objects from Multiple Demonstrations
Lu, H. (2015) · UC Berkeley · Cited by 2
Lin, T. (2015) · eScholarship (California Digital Library) · Cited by 2
Ganapathi, A., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Seita, D., Hoque, R., Gonzalez, J. E., & Goldberg, K. Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04339 · Cited by 1
“Acquiring useful visual state representations of deformable objects for manipulation is a central focus in prior work [8, 13, 16, 17, 19, 21].”
BackgroundLearning 6DoF Grasping Using Reward-Consistent DemonstrationPreprint
Kawakami, D., Ishikawa, R., Roxas, M., Sato, Y., & Oishi, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12321 · Cited by 3
“To address this issue, imitation learning, where in a robot learns from a human demonstration of its own operation, were proposed [5], [6].”
BackgroundDeformable Object Manipulation: Learning While Doing
McConachie, D. (2020) · Deep Blue (University of Michigan) · Cited by 2
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint CablesPreprint
Zhang, H., Ichnowski, J., Seita, D., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04840 · Cited by 2
“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].”
MethodTiboni, G., Camoriano, R., & Tommasi, T. (2026) · IEEE Transactions on Robotics · doi:10.1109/tro.2026.3699455 · Cited by 3
“Finally, our work focuses on learning paths that are generalizable across complex 3D shapes directly, a rarely addressed setting in robot IL [33], [42].”
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“The problem of determining the 2D or 3D configuration of articulated (or flexible) objects has originated much research work in the past few years [6], [7], [8], [9], [10], and different strategies have been employed as far as motion planning is concerned [11], [12], [13].”
Object Detection in Motion Reproduction System with Segmentation Algorithm
Sun, X., Nozaki, T., Ohnishi, K., & Murakami, T. (2022) · IEEE International Workshop on Advanced Motion Control (AMC) · doi:10.1109/amc51637.2022.9729308 · Cited by 1
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“Currently taught learning is mainly divided into two aspects: inverse reinforcement learning (IRL) [6] and behavioral cloning [7,8].”
BackgroundSoft contact grasping and orientation control of a rigid object using multibond graph
Rathee, R., Narwal, A. K., & Vaz, A. (2023) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-023-01305-9 · Cited by 1
“An approach using motion trajectory is also proposed to knot a rope, fold shirts, and pick up plates [17].”
Multi-Dimensional Deformable Object Manipulation Using Equivariant Models
Fu, T., Tang, Y. S., Wu, T., Xia, X., Wang, J., & Zhao, C. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341618 · Cited by 1
“Schulman et al. [22] propose a method called trajectory transfer to transfer expert-demonstrated trajectories from the geometry at training to the geometry at testing, and they use this method to tie five different shapes of knots.”
MethodGovindaraju, P. V. M., Dodiya, R., & Duraipandian, S. I. (2024) · International Conference on Control and Robotics (ICCR) · doi:10.1109/iccr64365.2024.10927594 · Cited by 1
“As a baseline comparison, we also evaluate the nearest neighbor policy as described by [2].”
InfluentialKadi, H. A., & Terzić, K. (2022) · Preprints.org · doi:10.20944/preprints202212.0305.v1 · Cited by 1
Alston, A., & Wo, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00078
Learning robotic manipulation from user demonstrations
Rahmatizadeh, R. (2017) · Journal of International Crisis and Risk Communication Research
Learn the Manipulation of Deformable Objects Using Tangent Space Point Set RegistrationPreprint
Wang, R., Tang, T., & Tomizuka, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.04077
“w, and can find non-rigid functions that register points better. Though point set registration is not designed directly for robotic learning from demonstration, it has proven to be a powerful tool. In [4], the TPS-RPM algorithm was utilized to teach robots to manipulate ropes. The point cloud of the training rope is ma…”
BackgroundMethodTask Generalization for Robust One-Shot Robot Programming using Entity-based Resources
Orendt, E. M., & Henrich, D. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525633
“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].”
BackgroundGhosn, Y., Kamareddine, M. H., salloum, G., et al. (2020) · Clinical surgery research communications · doi:10.31491/csrc.2020.06.051
Bertolucci, R., Capitanelli, A., Maratea, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06865
“The problem of determining the 2D or 3D configuration of articulated (or flexible) objects has originated much research work in the past few years [6], [7], [8], [9], [10], and different strategies have been employed as far as motion planning is concerned [11], [12], [13].”
Takamatsu, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-03243-2_869-1
Learning Autonomous Mobility Using Real Demonstration Data
Gu, J., & Li, Z. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659394
Takamatsu, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_869
Recognising Known Configurations of Garments For Dual-Arm Robotic Flattening
Duan, L., & Aragón-Camarasa, G. (2022) · International Conference on Robotics and Computer Vision (ICRCV) · doi:10.1109/icrcv55858.2022.9953186
Control of Linear Deformable Objects for Robotized Crops Manipulation
Aghajanzadeh, O. (2022) · theses.fr (ABES) · doi:10.70675/2c0a0ebcz2087z4de4z975azf01014ee2c81
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
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