What does the person feel? Learning to infer applied forces during robot-assisted dressing
Abstract
During robot-assisted dressing, a robot manipulates a garment in contact with a person's body. Inferring the forces applied to the person's body by the garment might enable a robot to provide more effective assistance and give the robot insight into what the person feels. However, complex mechanics govern the relationship between the robot's end effector and these forces. Using a physics-based simulation and data-driven methods, we demonstrate the feasibility of inferring forces across a person's body using only end effector measurements. Specifically, we present a long short-term memory (LSTM) network that at each time step takes a 9-dimensional input vector of force, torque, and velocity measurements from the robot's end effector and outputs a force map consisting of hundreds of inferred force magnitudes across the person's body. We trained and evaluated LSTMs on two tasks: pulling a hospital gown onto an arm and pulhng shorts onto a leg. For both tasks, the LSTMs produced force maps that were similar to ground truth when visualized as heat maps across the limbs. We also evaluated their performance in terms of root-mean-square error. Their performance degraded when the end effector velocity was increased outside the training range, but generalized well to limb rotations. Overall, our results suggest that robots could learn to infer the forces people feel during robot-assisted dressing, although the extent to which this will generalize to the real world remains an open question.
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Neural networks are convenient and effective at combining multi-modal sensory inputs, as well as integrating passive and interactive perception.
“NNs are also convenient and effective at combining multi-modal sensory inputs [55], as well as integrating passive and interactive perception [56,57,58].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [55]
Simulation becomes an ideal place for developing and testing dressing approaches.
“Hence, simulation becomes an ideal place for developing and testing dressing approaches [31,55,317].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [55]
Reliable force estimation is needed for safe and comfortable reactive manipulation.
“We still need reliable force estimation [32,55] for safe and comfortable reactive manipulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [55]
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“We still need reliable force estimation [32,55] for safe and comfortable reactive manipulation.”
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Self-citation
- Authors
- Erickson, Z., Clegg, A., Yu, W., Turk, G., Liu, C. K., & Kemp, C. C.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 6058-6065
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2737170915
- Cited by
- 41
- References
- 23
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 7 · 2022: 4 · 2021: 2 · 2020: 7 · 2019: 4 · 2018: 8 · 2017: 3
- FWCI
- 9.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- long short-term memory networks, physical simulation, hospital gowns, assistive robotics, human-robot interaction
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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