Humanoid robots hold great promise in assisting humans in diverse
environments and tasks, due to their flexibility and adaptability leveraging
human-like morphology. However, research in humanoid robots is often
bottlenecked by the costly and fragile hardware setups. To accelerate
algorithmic research in humanoid robots, we present a high-dimensional,
simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot
equipped with dexterous hands and a variety of challenging whole-body
manipulation and locomotion tasks. Our findings reveal that state-of-the-art
reinforcement learning algorithms struggle with most tasks, whereas a
hierarchical learning baseline achieves superior performance when supported by
robust low-level policies, such as walking or reaching. With HumanoidBench, we
provide the robotics community with a platform to identify the challenges
arising when solving diverse tasks with humanoid robots, facilitating prompt
verification of algorithms and ideas. The open-source code is available at
this https URL

为了加快人形机器人算法研究的进展，我们提出了一个基于高维度的模拟机器人学习基准，名为 HumanoidBench，该基准以配备熟练双手和各种具有挑战性的全身操作和运动任务的人形机器人为特色。我们的研究发现，当配备稳健的低级策略（如行走或伸手）时，最先进的强化学习算法在大多数任务中表现不佳，而分层学习基准则实现了优越的性能。通过 HumanoidBench，我们为机器人学界提供了一个平台，用于解决人形机器人在解决各种任务时所面临的挑战，促进算法和思想的快速验证。开源代码可在此网址获得：https://url

HumanoidBench: 模拟人型机器人全身运动与操作基准测试

HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion  and Manipulation

The challenge of teaching robots to perform dexterous manipulation, dynamic
locomotion, or whole--body manipulation from a small number of demonstrations
is an important research field that has attracted interest from across the
robotics community. In this work, we propose a novel approach by joining the
theories of Koopman Operators and Dynamic Movement Primitives to Learning from
Demonstration. Our approach, named \gls{admd}, projects nonlinear dynamical
systems into linear latent spaces such that a solution reproduces the desired
complex motion. Use of an autoencoder in our approach enables generalizability
and scalability, while the constraint to a linear system attains
interpretability. Our results are comparable to the Extended Dynamic Mode
Decomposition on the LASA Handwriting dataset but with training on only a small
fractions of the letters.

提出了一个新方法，通过结合 Koopman 操作符和动态运动原理与示教学习，将非线性动力系统投影到线性潜在空间中，从而实现复杂动作的解决方案。