analiza aiqbench
BiGym 2.0 establishes a critical benchmark for humanoid robotics by evaluating diverse learning strategies, including vision-language-action fine-tuning and coding agents, on complex whole-body household tasks. The results highlight that while agent-developed policies currently outperform demo-driven reinforcement learning baselines in bimanual reaching, significant challenges remain in cross-workspace stacking and multi-object transport. This open-source suite provides essential data for researchers aiming to advance embodied AI capabilities in dynamic environments.
BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation
arXiv:2610.07594v1 Announce Type: cross Abstract: Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of BiGym for the Unitree G1 across 20 household tasks using a unified whole-body controller for demonstration and evaluation. The suite provides 60 native human virtual-reality demonstrations per task with synchronised multi-camera views and full-body execution records. We benchmark vision-language-action fine-tuning, imitation learning, demo-driven reinforcement learning, and cold-start coding agents given the interaction budget of online reinforcement learning. With the same onboard views, proprioception and whole-body controller for every method, vision-language-action fine-tuning has the highest nine-task mean, and agent-developed programs outperform every demo-driven reinforcement learning baseline on this mean and lead on bimanual reaching. Cross-workspace stacking remains open, $\pi_{0.5}$ stays low on pick-box, and multi-object transport is hard for imitation learning, demo-driven reinforcement learning and coding agents. All environments, human demonstrations, and evaluation traces are open-sourced at https://github.com/swirl-uk/BiGym2.
Title:BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation
Abstract:Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of BiGym for the Unitree G1 across 20 household tasks using a unified whole-body controller for demonstration and evaluation. The suite provides 60 native human virtual-reality demonstrations per task with synchronised multi-camera views and full-body execution records. We benchmark vision-language-action fine-tuning, imitation learning, demo-driven reinforcement learning, and cold-start coding agents given the interaction budget of online reinforcement learning. With the same onboard views, proprioception and whole-body controller for every method, vision-language-action fine-tuning has the highest nine-task mean, and agent-developed programs outperform every demo-driven reinforcement learning baseline on this mean and lead on bimanual reaching. Cross-workspace stacking remains open, $\pi_{0.5}$ stays low on pick-box, and multi-object transport is hard for imitation learning, demo-driven reinforcement learning and coding agents. All environments, human demonstrations, and evaluation traces are open-sourced at this https URL.
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2610.07594 [cs.RO]
(or arXiv:2610.07594v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.07594 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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