Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning
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Abstract (arXiv)
Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose descriptions of physics which directly allow us to leverage optimization methods for physical reasoning and sequential manipulation planning. The proposed multi-physics formulation enables the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. As an essential ingredient, we propose a specific parameterization of wrench exchange between object surfaces in a path optimization framework, introducing the point-of-attack as decision variable. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1.
6 papers.
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fieldscited by 63
- 2021Learning Models as Functionals of Signed-Distance Fields for Manipulation Planningcited by 29
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approachescited by 19
- 2023I-PHYRE: Interactive Physical Reasoningcited by 15
- 2021Motion Planning by Learning the Solution Manifold in Trajectory Optimizationcited by 13
- 2023STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descentcited by 4
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches3 Apr 2024
- 2023I-PHYRE: Interactive Physical Reasoning4 Dec 2023
- 2023STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent3 Oct 2023
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fields24 Feb 2022
- 2021Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning2 Oct 2021
- 2021Motion Planning by Learning the Solution Manifold in Trajectory Optimization13 Jul 2021