Mediating between Contact Feasibility and Robustness of Trajectory Optimization through Chance Complementarity Constraints
This addresses contact uncertainty in robot motion planning, which is critical for real-world deployment, though it appears incremental as it builds on existing chance constraint methods.
The paper tackles motion planning for robots with intermittent contact under terrain and friction uncertainty by combining risk-sensitive objectives with chance constraints, demonstrating in push-block and hopper systems that this approach mediates a trade-off between robustness and constraint satisfaction with high feasibility guarantees.
As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our approach in two simple examples: a push-block system for benchmarking and a single-legged hopper. We demonstrate that chance constraints alone produce trajectories similar to those produced using strict complementarity constraints; however, when equipped with a robust objective, we show the chance constraints can mediate a trade-off between robustness to uncertainty and strict constraint satisfaction. Thus, our study may represent an important step towards reasoning about contact uncertainty in motion planning.