2000 character limit reached
Tactile-Informed Action Primitives Mitigate Jamming in Dense Clutter
Published 14 Feb 2024 in cs.RO | (2402.09564v1)
Abstract: It is difficult for robots to retrieve objects in densely cluttered lateral access scenes with movable objects as jamming against adjacent objects and walls can inhibit progress. We propose the use of two action primitives -- burrowing and excavating -- that can fluidize the scene to un-jam obstacles and enable continued progress. Even when these primitives are implemented in an open loop manner at clock-driven intervals, we observe a decrease in the final distance to the target location. Furthermore, we combine the primitives into a closed loop hybrid control strategy using tactile and proprioceptive information to leverage the advantages of both primitives without being overly disruptive. In doing so, we achieve a 10-fold increase in success rate above the baseline control strategy and significantly improve completion times as compared to the primitives alone or a naive combination of them.
- A. Pandey, S. Pandey, and D. Parhi, “Mobile robot navigation and obstacle avoidance techniques: A review,” Int Rob Auto J, vol. 2, no. 3, p. 00022, 2017.
- M. Stilman and J. J. Kuffner, “Navigation among movable obstacles: Real-time reasoning in complex environments,” Int. J. Humanoid Robot, vol. 2, no. 04, pp. 479–503, 2005.
- M. Stilman, J.-U. Schamburek, J. Kuffner, and T. Asfour, “Manipulation planning among movable obstacles,” in IEEE ICRA, 2007, pp. 3327–3332.
- M. Wang, R. Luo, A. Ö. Önol, and T. Padir, “Affordance-based mobile robot navigation among movable obstacles,” in IEEE IROS, 2020, pp. 2734–2740.
- K. Ellis, H. Zhang, D. Stoyanov, and D. Kanoulas, “Navigation among movable obstacles with object localization using photorealistic simulation,” in IEEE IROS, 2022, pp. 1711–1716.
- S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel, “Combined task and motion planning through an extensible planner-independent interface layer,” in IEEE ICRA, 2014, pp. 639–646.
- Muhayyuddin, M. Moll, L. Kavraki, J. Rosell, et al., “Randomized physics-based motion planning for grasping in cluttered and uncertain environments,” IEEE RA-L, vol. 3, no. 2, pp. 712–719, 2017.
- B. Huang, S. D. Han, J. Yu, and A. Boularias, “Visual foresight trees for object retrieval from clutter with nonprehensile rearrangement,” IEEE RA-L, vol. 7, no. 1, pp. 231–238, 2021.
- M. Gupta, T. Rühr, M. Beetz, and G. S. Sukhatme, “Interactive environment exploration in clutter,” in IEEE IROS, 2013, pp. 5265–5272.
- H. Huang, M. Dominguez-Kuhne, V. Satish, M. Danielczuk, K. Sanders, J. Ichnowski, A. Lee, A. Angelova, V. Vanhoucke, and K. Goldberg, “Mechanical search on shelves using lateral access x-ray,” in IROS. IEEE, 2021, pp. 2045–2052.
- H. Huang, M. Danielczuk, C. M. Kim, L. Fu, Z. Tam, J. Ichnowski, A. Angelova, B. Ichter, and K. Goldberg, “Mechanical search on shelves using a novel “bluction” tool,” in IEEE ICRA, 2022, pp. 6158–6164.
- A. Schneider, J. Sturm, C. Stachniss, M. Reisert, H. Burkhardt, and W. Burgard, “Object identification with tactile sensors using bag-of-features,” in IEEE IROS, 2009, pp. 243–248.
- D. Xu, G. E. Loeb, and J. A. Fishel, “Tactile identification of objects using bayesian exploration,” in IEEE ICRA, 2013, pp. 3056–3061.
- J. Lin, R. Calandra, and S. Levine, “Learning to identify object instances by touch: Tactile recognition via multimodal matching,” in IEEE ICRA, 2019, pp. 3644–3650.
- S. Zhong, N. Fazeli, and D. Berenson, “Soft tracking using contacts for cluttered objects to perform blind object retrieval,” IEEE RA-L, vol. 7, no. 2, pp. 3507–3514, 2022.
- A. Jain, M. D. Killpack, A. Edsinger, and C. C. Kemp, “Reaching in clutter with whole-arm tactile sensing,” IJRR, vol. 32, no. 4, pp. 458–482, 2013.
- T. Bhattacharjee, P. M. Grice, A. Kapusta, M. D. Killpack, D. Park, and C. C. Kemp, “A robotic system for reaching in dense clutter that integrates model predictive control, learning, haptic mapping, and planning.” Georgia Institute of Technology, 2014.
- M. D. Killpack, A. Kapusta, and C. C. Kemp, “Model predictive control for fast reaching in clutter,” Autonomous Robots, vol. 40, pp. 537–560, 2016.
- A. Albini, F. Grella, P. Maiolino, and G. Cannata, “Exploiting distributed tactile sensors to drive a robot arm through obstacles,” IEEE RA-L, vol. 6, no. 3, pp. 4361–4368, 2021.
- T. Iberall, “The nature of human prehension: Three dextrous hands in one,” in IEEE ICRA, vol. 4, 1987, pp. 396–401.
- D. Ortiz, N. Gravish, and M. T. Tolley, “Soft robot actuation strategies for locomotion in granular substrates,” IEEE RA-L, vol. 4, no. 3, pp. 2630–2636, 2019.
- R. Das, S. P. M. Babu, A. Mondini, and B. M. Mazzolai, “Effects of lateral undulation in granular medium burrowing with a peristaltic soft robot,” in IEEE RoboSoft, 2023, pp. 1–6.
- H. Choi, D. Brouwer, M. A. Lin, K. T. Yoshida, C. Rognon, B. Stephens-Fripp, A. M. Okamura, and M. R. Cutkosky, “Deep learning classification of touch gestures using distributed normal and shear force,” in IEEE IROS, 2022, pp. 3659–3665.
- J. Liang, S. Saxena, and O. Kroemer, “Learning active task-oriented exploration policies for bridging the sim-to-real gap,” arXiv preprint arXiv:2006.01952, 2020.
- A. Church, J. Lloyd, R. Hadsell, and N. F. Lepora, “Tactile sim-to-real policy transfer via real-to-sim image translation,” in CoRL, ser. Proceedings of Machine Learning Research, vol. 164. PMLR, 08–11 Nov 2022, pp. 1645–1654.
- A. Morales, M. Prats, P. Sanz, and A. P. Pobil, “An experiment in the use of manipulation primitives and tactile perception for reactive grasping,” in RSS Workshop on Robot Manipulation, Atlanta, USA, 2007.
- F. R. Hogan, “Reactive manipulation with contact models and tactile feedback,” Ph.D. dissertation, Massachusetts Institute of Technology, 2020.
- R. Thomasson, E. Roberge, M. R. Cutkosky, and J.-P. Roberge, “Going in blind: Object motion classification using distributed tactile sensing for safe reaching in clutter,” in IEEE IROS, 2022, pp. 1440–1446.
- X. Zhang, S. Jin, C. Wang, X. Zhu, and M. Tomizuka, “Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks,” in IEEE ICRA, 2022, pp. 9881–9887.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.