ULTRA：统一的多模态控制用于自主人形全身体姿Manipulation
ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation
AI总结：
ULTRA通过统一多模态控制框架，实现了基于感知和高层任务规范的自主人形全身体姿Manipulation，优于传统跟踪方法。
摘要：
实现自主且多功能的全身体姿Manipulation仍然是使人形机器人在实际中实用的核心障碍。然而，现有方法从根本上受到限制：重新定位的数据通常稀缺或质量低；方法难以扩展到大规模技能库；最重要的是，它们依赖于跟踪预定义的运动参考，而不是从感知和高层任务规范中生成行为。为了解决这些限制，我们提出了ULTRA，一个统一的框架，包含两个关键组件。首先，我们引入了一种基于物理的神经重新定位算法，将大规模动作捕捉数据转换为人形 embodiment，同时在接触丰富的交互中保持物理合理性。其次，我们学习了一个统一的多模态控制器，支持密集参考和稀疏任务规范，在从准确的动作捕捉状态到嘈杂的以自身为中心的视觉输入的传感范围内。我们将在该控制器中蒸馏出一个通用的跟踪策略，将运动技能压缩到一个紧凑的潜在空间，并应用强化学习微调来扩展覆盖范围并提高在分布外场景下的鲁棒性。这使稀疏意图下能够协调全身体行为而无需测试时的参考运动。我们评估了ULTRA在仿真和真实Unitree G1人形机器人上的表现。结果表明，ULTRA能够从自身感知中实现自主、目标导向的全身体姿Manipulation，一致地优于具有有限技能的跟踪-only基线。
英文摘要：
Achieving autonomous and versatile whole-body loco-manipulation remains a central barrier to making humanoids practically useful. Yet existing approaches are fundamentally constrained: retargeted data are often scarce or low-quality; methods struggle to scale to large skill repertoires; and, most importantly, they rely on tracking predefined motion references rather than generating behavior from perception and high-level task specifications. To address these limitations, we propose ULTRA, a unified framework with two key components. First, we introduce a physics-driven neural retargeting algorithm that translates large-scale motion capture to humanoid embodiments while preserving physical plausibility for contact-rich interactions. Second, we learn a unified multimodal controller that supports both dense references and sparse task specifications, under sensing ranging from accurate motion-capture state to noisy egocentric visual inputs. We distill a universal tracking policy into this controller, compress motor skills into a compact latent space, and apply reinforcement learning finetuning to expand coverage and improve robustness under out-of-distribution scenarios. This enables coordinated whole-body behavior from sparse intent without test-time reference motions. We evaluate ULTRA in simulation and on a real Unitree G1 humanoid. Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception, consistently outperforming tracking-only baselines with limited skills.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
