APEX: Learning Adaptive High-Platform Traversal for Humanoid Robots

• URL: http://arxiv.org/abs/2602.11143v2
• Date: Fri, 06 Mar 2026 05:01:58 GMT
• Title: APEX: Learning Adaptive High-Platform Traversal for Humanoid Robots
• Abstract summary: We propose APEX, a system for perceptive, climbing-based high-platform traversal behaviors.<n>Central to our approach is a generalized ratchet progress reward for learning contact-rich, goal-reaching maneuvers.<n>We train LiDAR-based full-body maneuver policies and reduce the sim-to-real perception gap.
• Score: 32.84976822343718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humanoid locomotion has advanced rapidly with deep reinforcement learning (DRL), enabling robust feet-based traversal over uneven terrain. Yet platforms beyond leg length remain largely out of reach because current RL training paradigms often converge to jumping-like solutions that are high-impact, torque-limited, and unsafe for real-world deployment. To address this gap, we propose APEX, a system for perceptive, climbing-based high-platform traversal that composes terrain-conditioned behaviors: climb-up and climb-down at vertical edges, walking or crawling on the platform, and stand-up and lie-down for posture reconfiguration. Central to our approach is a generalized ratchet progress reward for learning contact-rich, goal-reaching maneuvers. It tracks the best-so-far task progress and penalizes non-improving steps, providing dense yet velocity-free supervision that enables efficient exploration under strong safety regularization. Based on this formulation, we train LiDAR-based full-body maneuver policies and reduce the sim-to-real perception gap through a dual strategy: modeling mapping artifacts during training and applying filtering and inpainting to elevation maps during deployment. Finally, we distill all six skills into a single policy that autonomously selects behaviors and transitions based on local geometry and commands. Experiments on a 29-DoF Unitree G1 humanoid demonstrate zero-shot sim-to-real traversal of 0.8 meter platforms (approximately 114% of leg length), with robust adaptation to platform height and initial pose, as well as smooth and stable multi-skill transitions.

Related papers

• Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking [78.30338456206984]
  We introduce a novel framework for training general-purpose 3D foothold-tracking policies.<n>By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains.<n>Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation.
  arXiv  Detail & Related papers  (2026-06-06T16:44:56Z)
• TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion [1.3553462938197818]
  Foothold selection on structured terrain requires explicit reasoning about contact planarity, surface steepness, and kinematic reachability.<n>We propose a multi-channel terrain cost combining flatness, steepness, and velocity-aware height feasibility, plus a forward climb reward.
  arXiv  Detail & Related papers  (2026-06-06T10:25:13Z)
• LadderMan: Learning Humanoid Perceptive Ladder Climbing [56.550077812088404]
  We present textbfLadderMan, a unified system that enables humanoid robots to robustly climb diverse ladders.<n>Our climbing policy is built on a scalable two-stage learning pipeline, where we use hybrid motion tracking to learn multiple climbing experts.<n>We further train a separate manipulation policy using a dual-agent formulation, allowing stable on-ladder manipulation via teleoperation.
  arXiv  Detail & Related papers  (2026-06-04T08:47:08Z)
• Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion [15.718523500563203]
  This paper proposes an explicit stair geometry conditioning framework for robust humanoid stair climbing.<n>Instead of encoding terrain as high-dimensional latent features, we extract a compact set of interpretable geometric parameters, including step height, step depth, and current yaw angle relative to the robot heading.<n>These explicit stair parameters directly condition a Proximal Policy Optimization (PPO)-based locomotion policy, enabling proactive modulation of swing-foot clearance and stride characteristics according to stair structure.
  arXiv  Detail & Related papers  (2026-05-11T03:47:24Z)
• TransCurriculum: Multi-Dimensional Curriculum Learning for Fast & Stable Locomotion [50.54752207285298]
  TransCurriculum is a transformer-based multi-dimensional curriculum learning approach for agile quadrupedal locomotion.<n>We validate our approach on the Unitree Go1 robot in simulation and deploy it zero-shot on Go1 hardware.
  arXiv  Detail & Related papers  (2026-03-14T23:44:42Z)
• Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching [77.28042137892943]
  We present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour.<n>We train motion-tracking reinforcement learning expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy.<n>We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot.
  arXiv  Detail & Related papers  (2026-02-17T18:59:11Z)
• FastStair: Learning to Run Up Stairs with Humanoid Robots [19.796528911795516]
  Running up stairs is effortless for humans but difficult for humanoid robots.<n>FastStair is a planner-guided, multi-stage learning framework to achieve fast and stable stair ascent.<n>We deploy the resulting controller on the Oli humanoid robot, achieving stable stair ascent at commanded speeds up to 1.65 m/s.
  arXiv  Detail & Related papers  (2026-01-15T13:14:59Z)
• Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction [86.53248703859718]
  We introduce a perceptive locomotion framework that merges terrain sensing, gait regulation, and whole-body control into a single reinforcement learning policy.<n>Experiments conducted on a 31-DoF, 1.65 m humanoid robot demonstrate robust locomotion in both simulation and real-world settings.
  arXiv  Detail & Related papers  (2025-12-08T11:42:41Z)
• PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion [41.99844472131922]
  Phase-Guided Terrain Traversal (PGTT) is a perception-aware deep-RL approach that enforces gait structure purely through reward shaping.<n>Trained in MuJoCo (MJX) on procedurally generated stair-like terrains with curriculum and domain randomization, PGTT achieves the highest success under push disturbances.
  arXiv  Detail & Related papers  (2025-10-21T07:00:18Z)
• BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds [35.62230804783507]
  Existing learning-based approaches often struggle on complex terrains due to sparse foothold rewards and inefficient learning processes.<n>We introduce BeamDojo, a reinforcement learning framework designed for enabling agile humanoid locomotion on sparse footholds.<n>We show that BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world.
  arXiv  Detail & Related papers  (2025-02-14T18:42:42Z)
• Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot [7.516046071926082]
  We propose a new framework to mitigate the gap between simulated and real robots. Our framework offers a structured guideline for parameter selection and the range for dynamics randomization in simulation. Results are, to the best of our knowledge, one of the highest and longest running jumps demonstrated by an RL-based control policy in a real quadruped robot.
  arXiv  Detail & Related papers  (2024-04-23T14:52:09Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Robust and Versatile Bipedal Jumping Control through Reinforcement Learning [141.56016556936865]
  This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions. We develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history.
  arXiv  Detail & Related papers  (2023-02-19T01:06:09Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.