Humanoid-Bench: Robotic Benchmark Suite

• Humanoid-Bench is a comprehensive benchmark suite for humanoid robotic learning, perception, and interaction that integrates diverse tasks and multimodal sensor data.
• It offers over 10,000 teleoperated trajectories covering 260 tasks and supports both real and simulated evaluations through standardized protocols.
• The platform employs a combination of human teleoperation and LLM-driven autonomous generation with cloud-based evaluation to advance robotic policy learning.

Humanoid-Bench refers to a set of large-scale, systematic benchmarks and datasets designed for the evaluation and acceleration of humanoid robot learning, perception, and interaction. The term encompasses multiple research efforts with partially overlapping terminology and scope, most notably “Humanoid Everyday” (Zhao et al., 9 Oct 2025), “HumanoidGen” (Jing et al., 1 Jul 2025), “HumanoidBench” (Sferrazza et al., 2024), and related platforms in multimodal search, human understanding, and physical benchmarking. Together, these resources provide the robotics and machine learning communities with high-diversity datasets, standardized evaluation protocols, and robust infrastructure for measuring manipulation, locomotion, social interaction, scene understanding, and policy competence in humanoid robots.

1. Scope and Dataset Composition

Humanoid-Bench benchmar ks address key limitations of prior robotics data, which are predominantly focused on stationary arms or single-modality tasks. In contrast, modern Humanoid-Bench releases emphasize:

• Diversity of tasks: 10,300 real-robot teleoperated trajectories covering 260 unique tasks and 7 high-level categories, including basic, deformable, articulated manipulation, tool use, high-precision operations, human–robot interaction, and combined locomotion-manipulation (loco-manipulation) (Zhao et al., 9 Oct 2025).
• Sensor richness: Multimodal data synchronized at 30 Hz (sub-ms timestamping), including egocentric RGB (Intel RealSense up to 1280×720), depth (IR stereo), 3D LiDAR (Livox Puck), high-resolution 5-finger tactile skins, body IMUs, joint-level state and torques, and textual natural-language task annotations.
• Bimanual dexterity: Simulation-based variants (e.g., HumanoidGen/HGen-Bench) extend to 20 dexterous tabletop manipulation tasks with up to 26 DoF (14 in arms, 12 in hands), annotated with per-asset and per-hand keypoints/axes, supporting both single-arm and bimanual sequences (Jing et al., 1 Jul 2025).
• Whole-body interaction: Simulated benchmarks such as HumanoidBench (Sferrazza et al., 2024) provide high-dimensional tasks involving full-body locomotion, object transport, multi-contact behaviors, and complex environment traversal, with action spaces as large as 61 DoF per timestep.

Tasks range from long-horizon, contact-rich sequences (e.g., walking to and opening a door) to high-precision operations (e.g., needle threading), and incorporate both human-robot collaborative and solo behaviors.

2. Data Collection and Annotation Pipeline

The acquisition of diverse, high-fidelity demonstration data is central to the reliability of Humanoid-Bench benchmarks:

• Teleoperation: "Humanoid Everyday" uses human operators donning Apple Vision Pro headsets, combining visualized hand/finger tracking and retargeting via inverse kinematics (Pinocchio solver) to drive full-body Unitree humanoids in real time. Asynchronous, multi-process design achieves loop latencies <2 ms, enabling tight coupling of sensory input and robot control (Zhao et al., 9 Oct 2025).
• Language annotation: After each episode, operators provide free-form textual descriptions that are precisely temporally aligned with sensor logs, supporting multimodal learning and grounding.
• Autonomous generation: HumanoidGen applies LLM-driven planners and atomic operation libraries to automatically synthesize thousands of high-quality, spatially constrained demonstrations, including LLM-based scene generation, relational constraint planning, and aggressive use of Monte Carlo Tree Search (MCTS) and the STCR (Segment-Truncate-Combine-Resume) framework for trajectory diversity and recovery from planning failures (Jing et al., 1 Jul 2025).
• Standardization: All perceptual streams (visual, tactile, kinesthetic, linguistic) are tightly time-aligned and open-sourced as reproducible packages.

3. Benchmarking, Evaluation, and Cloud Inference Platform

Humanoid-Bench supports transparent, reproducible assessment of learned policies via:

• Cloud-based evaluation: A policy server connects to the robot controller over TCP/IP. Robot state (RGB, depth, full-body kinematics) is streamed in real-time; the client runs inference and sends back 30 Hz control packets for actuation. Trial states are observable live or logged for later analysis (Zhao et al., 9 Oct 2025).
• Task protocols: For each evaluated task (a subset of the 260+), environments are identically reset with fixed object layouts, lighting, and distractors. Manual resets are required between trials; continuous runs up to 100 min with rare human interventions demonstrate system stability.
• Success metrics:
  • Success rate $$S=\frac{\text{\# successful trials}}{\text{\# total trials}}$$
  • Average return $R=\frac{1}{N} \sum_{i=1}^{N} r_i$, with $r_i$ binary per-trial reward (1/0)
  • Per-category analyses highlight chronic difficulty in high-precision insertion/loco-manipulation tasks (0% success for insertion across all baselines).
• Baseline performance:
  • Diffusion Policy (DP): $S\approx29\%$
  • 3D Diffusion Policy (DP3): $S\approx34\%$
  • ACT, OpenVLA, π₀-FAST, GR00T N1.5 reach higher, with GR00T N1.5 attaining $S\approx51\%$ on average tasks (Zhao et al., 9 Oct 2025).

The simulation-based benchmarks (e.g., HumanoidBench, HGen-Bench) enable high-throughput validation of RL and imitation learning methods, and support both model-based (DreamerV3, TD-MPC2) and model-free (SAC, PPO) approaches (Sferrazza et al., 2024, Jing et al., 1 Jul 2025).

4. Methodological Design Principles

Humanoid-Bench benchmarks are constructed with a focus on:

• High-dimensionality and multimodality: Handling 28–61 DoF action spaces and combining vision, touch, proprioception, and language presents significant challenges for both data-driven and model-based policy learning. Action chunking and diffusion policies are used to address the brittleness induced by high-dimensional imitation learning targets.
• Hierarchical decomposition: Hierarchical RL, with robust low-level (e.g., walking/reaching) policies and high-level task planners, demonstrates superior sample efficiency and task success in large state-action spaces (Sferrazza et al., 2024). Modularization also extends to atomic dexterous operations and LLM-generated spatial reasoning in synthetic data pipelines (Jing et al., 1 Jul 2025).
• Collision and contact management: Active and dynamic collision avoidance primitives in demonstration generation drastically reduce failures, especially in contact-rich, articulated manipulation (e.g., drawer pulls) (Jing et al., 1 Jul 2025).
• Dataset scaling: Empirical results show that policy performance improves consistently with increases in demonstration count, especially for diffusion-based methods; few-shot generalization is more feasible for simpler tasks than long-horizon multi-object scenarios (Zhao et al., 9 Oct 2025, Jing et al., 1 Jul 2025).

5. Limitations, Implementation Caveats, and Current Challenges

Despite the comprehensive nature of the Humanoid-Bench suite, several structural and operational limitations are acknowledged:

• Scene resetting: Automated environment resets are not yet implemented—human intervention is required, which limits throughput and reproducibility for high-frequency, parallelized benchmarking (Zhao et al., 9 Oct 2025).
• Sensor calibration: Tactile and LiDAR sensors require periodic recalibration to avoid drift during long-term experiments.
• Motor performance: Extended sessions can induce motor overheating on hardware platforms, necessitating periodic rest cycles.
• Sim-to-real robustness: Real-robot and simulated domains differ in noise and system response; domain randomization and robust controller initialization are active areas of future study (Sferrazza et al., 2024).
• High-dimensionality bottleneck: Policy learning in large action/spatial domains remains sample-inefficient; ablations show that masking irrelevant DoFs produces large speedups but may forgo task-critical dexterity (Sferrazza et al., 2024, Zhao et al., 9 Oct 2025).
• Diversity bias: Task/scene diversity is extensive but not exhaustive; generalization to radically new environments or physical affordances is an outstanding research problem.

6. Research Horizons and Future Directions

Humanoid-Bench benchmarks are evolving, with directions proposed toward:

• Multimodal fusion: Integrating touch, vision, and language for richer policy grounding, with active exploration of language-annotated demonstrations and vision–language–action models.
• Autonomous demonstration generation: Scaling up autonomous, LLM-driven, or simulated demonstration pipelines for more sophisticated dexterous and multi-contact tasks (Jing et al., 1 Jul 2025).
• Automated scene resetting and evaluation: Developing self-resetting robotic environments for fully unattended benchmarking and regression testing.
• Expanded dexterity: Incorporating manipulation primitives such as screwing, furniture assembly, and advanced tool use; extending to general-purpose humanoid agents in unconstrained real-world scenarios.
• Sim-to-real transfer: Advances in domain randomization and closed-loop, cloud-based evaluation will further bridge the gap between simulation and hardware, enabling scalable validation of policy generalization.

By providing open-source, reproducible pipelines and a diverse, multimodal dataset, Humanoid-Bench serves as a unifying testbed for the systematic evaluation, comparison, and advancement of general-purpose humanoid manipulation, perception, and learning (Zhao et al., 9 Oct 2025, Jing et al., 1 Jul 2025, Sferrazza et al., 2024).