Stable Object Placement Planning from Contact Point Robustness
Philippe Nadeau, Jonathan Kelly
AI summary
Problem
Stably placing rigid objects in complex scenes is computationally challenging due to NP-hard contact force calculations, while existing methods often ignore inertial parameters or rely on slow, shape-restricted heuristics.
Approach
The planner reverses traditional sampling by selecting contact points first and then computing a placement pose that solicits them, guided by a physics-based static robustness map that evaluates stability without combinatorial complexity.
Key results
- 20× faster than baseline algorithms without the robustness heuristic
- 8× faster than state-of-the-art sample-and-evaluate planners
- Higher success rate in finding stable placements across 1,500+ simulations
- Validated in 10 real-world robot experiments with 50 successful placements
Why it matters
Provides a scalable, shape-agnostic solution for automating stable object assembly in domestic, industrial, and outdoor robotic tasks.
Abstract
We introduce a planner designed to guide robot manipulators in stably placing objects within complex scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. The proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our approach provides a general and scalable solution to the problem of rigid object placement planning.