外观
论文arXiv 2610.0832022 天前
Humanoid Horizon框架突破人形机器人长程全身操作瓶颈
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Humanoid Horizon: Extending Task Horizon in Whole-Body Loco-Manipulation via Parallel Training, Dynamic Starting, and Reward Gating（arXiv 2610.08320）
核心内容
针对杂乱室内环境中人形机器人连续导航、抓取、搬运及放置的长程全身操作难题，研究团队提出Humanoid Horizon统一策略框架。该方法旨在解决传统强化学习中因过度关注早期阶段导致的Easy-reward Bias及后期性能衰退的灾难性遗忘问题。在包含350个训练场景与66个测试场景的LHM-Humanoid基准上，该框架实现了双物体连续运输任务单阶段成功率超过80%的量化指标，且在物体数量增加时表现出比所有基线更平滑的性能衰减曲线。
技术方案包含三大核心机制：Parallel Training Strategy将N个场景组织为S个并发阶段流并共享策略，确保各运输阶段获得持续梯度更新；Dynamic Starting Mechanism利用上游Rollout的终止状态更新环境初始状态，逐步扩大状态转移覆盖范围以增强阶段边界鲁棒性；Reward Gating机制在当前置放物体位移超阈值时将后续奖励归零，迫使共享策略学习避免扰动已放置物体。三者协同有效解决了长时序任务中的优化瓶颈与状态保持难题。
关键看点
- 并行训练策略：将多场景组织为并发阶段流并由共享策略控制，消除顺序优化瓶颈，确保全流程梯度持续更新
- 动态起始机制：利用上游轨迹终止状态初始化环境，逐步扩展状态转移覆盖面，显著提升阶段切换处的策略鲁棒性
- 奖励门控机制：当已放置物体位移超阈值时截断后续奖励，强制策略保持对既有成果的维护，防止灾难性遗忘
- 基准测试性能：在LHM-Humanoid双物体任务中单阶段成功率超80%，长程任务性能衰减幅度显著低于现有基线
工程与产业观察
该工作为人形机器人长程复杂操作提供了可复用的RL算法范式。其并行训练与奖励门控设计直接回应了具身智能从仿真到真机部署中长序列任务失败率高的痛点，证明了在不依赖大规模预训练模型前提下，通过精细化训练工程优化即可大幅提升Whole-body Control在结构化任务中的可靠性，对工业物流等场景落地具有明确参考价值。
英文摘要 (Abstract)
Cluttered indoor environments, where large and heavy objects are scattered across diverse surfaces, require humanoid robots to sequentially navigate, grasp, transport, and accurately place each item at its target location within a single uninterrupted episode. This long-horizon, whole-body loco-manipulation task remains a significant challenge for current methods. Previous approaches often suffer from two main issues: easy-reward bias, where training overemphasizes early transport stages at the expense of later ones, and catastrophic forgetting, where focusing on later stages leads to a decline in earlier-stage performance. In this work, we introduce Humanoid Horizon, a unified policy framework designed to overcome these limitations through three interrelated mechanisms. The Parallel Training Strategy organizes $N$ scenes into $S$ concurrent stage streams governed by a shared policy, ensuring all transport stages receive continuous gradient updates and removing the bottleneck of sequential optimization. The Dynamic Starting Mechanism updates each environment's initial state with terminal states from upstream rollouts, gradually broadening transition coverage and enhancing robustness at stage boundaries. Reward Gating sets the reward to zero for the rest of the episode in later-stage streams when the immediately preceding object is displaced beyond a set threshold, so the shared policy learns not to disturb a just-placed object and earlier placements are preserved throughout the episode. Collectively, these strategies achieve per-stage success rates exceeding 80% on the two-object LHM-Humanoid benchmark (350 training scenes, 66 held-out scenes). As the number of sequentially transported objects grows beyond two, success declines with the horizon, but the degradation is graceful relative to the sharp drop seen in all baselines.
作者：Haozhuo Zhang, Qiang Zhang, Jian Tang, Mingzhe Ni, Michele Caprio, Angelo Cangelosi, Wei Pan
来源
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