Assistax: A Multi-Agent Hardware-Accelerated RL Benchmark for Assistive Robotics
Assistax is a new GPU-accelerated reinforcement learning benchmark for assistive robotics, built on JAX and MuJoCo-MJX. It supports multi-agent RL with a simulated human partner and introduces an ad-hoc teamwork pipeline for evaluating robot policies with unseen human policies. The benchmark achieves up to 412x faster simulation than comparable CPU-based environments and provides pre-trained humanoid policies for research use.
Why it matters: Assistax enables high-throughput, multi-agent RL research in assistive robotics, addressing a key bottleneck in developing and evaluating human-robot interaction policies.
Full story at: arXiv Multiagent Systems ↗