Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 6513 - 6520
• Main Authors: Dong, Yifei, Pokorny, Florian T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Cognition; Fixtures; Heuristic algorithms; Potential energy; Robotics and automation; Three-dimensional displays
• DOI: 10.1109/ICRA57147.2024.10611593

We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional configuration spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional configuration spaces, but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We present 8 simulation experiments demonstrating the applicability of our approach to various complex quasi-static manipulation scenarios. Quantitative results indicate the effectiveness of our approach in providing upper-bound estimates for escape energy in quasi-static manipulation scenarios. Two real-world experiments also show that the computed normalized escape energy estimates appear to correlate strongly with the probability of escape of an object under randomized pose perturbation 1 .