Papers accepted at Humanoids 2024
Two papers accepted at Humanoids 2024, being held in Nancy, France
Victor Leve, Joao Moura, Namiko Saito, Steve Tonneau and Sethu Vijayakumar, Explicit Contact Optimization in Whole-Body Contact-Rich Manipulation, Proc. IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids 2024), Nancy, France (2024). [pdf] [video] [citation]
Humans can exploit contacts anywhere on their body surface to manipulate large and heavy items, objects normally out of reach or multiple objects at once. However, such manipulation through contacts using the whole surface of the body remains extremely challenging to achieve on robots. This can be labelled as Whole-Body Contact-Rich Manipulation (WBCRM) problem. In addition to the high-dimensionality of the Contact-Rich Manipulation problem due to the combinatorics of contact modes, admitting contact creation anywhere on the body surface adds complexity, which hinders planning of manipulation within a reasonable time. We address this computational problem by formulating the contact and motion planning of planar WBCRM as hierarchical continuous optimization problems. To enable this formulation, we propose a novel continuous explicit representation of the robot surface, that we believe to be foundational for future research using continuous optimization for WBCRM. Our results demonstrate a significant improvement of convergence, planning time and feasibility – with, on the average, 99% less iterations and 96% reduction in time to find a solution over considered scenarios, without recourse to prone-to-failure trajectory refinement steps.
Jiayi Wang, Saeid Samadi, Hefan Wang, Pierre Fernbach, Olivier Stasse, Sethu Vijayakumar and Steve Tonneau, NAS: N-step computation of All Solutions to the footstep planning problem, Proc. IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids 2024), Nancy, France (2024). [pdf] [video] [citation]
How many ways are there to climb a staircase in a given number of steps? Infinitely many, if we focus on the continuous aspect of the problem. A finite, possibly large number if we consider the discrete aspect, i.e. on which surface which effectors are going to step and in what order. We introduce NAS, an algorithm that considers both aspects simultaneously and computes all the possible solutions to such a contact planning problem, under standard assumptions. To our knowledge NAS is the first algorithm to produce a globally optimal policy, efficiently queried in real time for planning the next footsteps of a humanoid robot. Our empirical results (in simulation and on the Talosplatform) demonstrate that, despite the theoretical exponential complexity, optimisations reduce the practical complexity of NAS to a manageable bilinear form, maintaining completeness guarantees and enabling efficient GPU parallelisation. NAS is demonstrated in a variety of scenarios for the Talos robot, both in simulation and on the hardware platform. Future work will focus on further reducing computation times and extending the algorithm’s applicability beyond gaited locomotion.