Presentation @ IROS 2020.

Journal article accepted to IEEE Robotics and Automation Letters (RA-L) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020 in Las Vegas, USA:
Paper: [ Ссылка ]
Pre-print: [ Ссылка ]

[Abstract]
We present a motion planning algorithm with probabilistic guarantees for limbed robots with stochastic gripping forces. Planners based on deterministic models with a worst-case uncertainty can be conservative and inflexible to consider the stochastic behavior of the contact, especially when a gripper is installed. Our proposed planner enables the robot to plan its pose and contact force trajectories simultaneously while considering the risk associated with the gripping forces. Our planner is formulated as a nonlinear programming problem with chance constraints, which allows the robot to generate a variety of motions based on different risk bounds. To model the gripping forces as random variables, we employ Gaussian Process regression. We validate our proposed motion planning algorithm on an 11.5 kg six-limbed robot for two-wall climbing. Our results show that our proposed planner generates various trajectories (e.g., avoiding low friction terrain with low risk bound, choosing unstable but fast gait with high risk bound) by changing the probability of risk based on various specifications.

[Timestamps]
00:00 Introduction
00:42 Challenges
02:16 High-level idea of our contributions
04:40 Chance-constrained trajectory optimization
06:34 Stochastic friction cone
07:18 Chance constraints
08:46 Gaussian Process
09:38 Hardware experiments
13:03 Conclusion

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