Related papers: Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning

Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning

URL: http://arxiv.org/abs/2608.17320v1
Date: Tue, 18 Aug 2026 03:24:50 GMT
Title: Robust Brachiation on a Life-Sized Dual-Arm Robot Using Waypoint-Guided Reinforcement Learning
Abstract summary: Brachiation is a form of locomotion in which primates move primarily using their arms.<n>We present a reinforcement learning-based method to realize brachiation on a life-sized dual-arm robot.
Score: 11.876800221374758
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Brachiation is a form of locomotion in which primates move primarily using their arms, enabling traversal in environments without footholds. However, this motion requires highly coordinated whole-body movement and precise timing control for bar grasping and release. As a result, achieving robust behavior on life-sized robotic platforms remains challenging. In this study, we present a reinforcement learning-based method to realize brachiation on a life-sized dual-arm robot. The core of the proposed approach is Waypoint-Guided Reinforcement Learning (WGRL), a learning framework for inducing non-linear and complex motions. For high-difficulty tasks where imitation learning data are unavailable, WGRL guides behavior acquisition by sparsely specifying waypoints for the end-effector trajectory, while whole-body motion is generated through reinforcement learning. In addition, by integrating the waypoint-following guidance with rewards based on task success and mechanical energy, and training in an environment designed for Sim-to-Real transfer, the proposed method achieves both forward progression and motion stability. The acquired behavior is evaluated through Sim-to-Sim experiments under monkey-bar environments with geometric variations and hardware experiments, confirming robust brachiation including failure recovery behavior. This study provides effective learning design guidelines for realizing arm-based locomotion on life-sized robotic hardware and expanding the traversable workspace of robots.

Related papers

CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements [63.40365657068892]
Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.<n>Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions.<n>In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations.
arXiv Detail & Related papers (2026-09-07T13:47:22Z)
Learning Whole-Body Control for a Salamander Robot [1.321203201549798]
Amphibious legged robots inspired by salamanders are promising in applications in complex amphibious environments.<n>Most salamander robots relied on central-pattern-generator (CPG)-based and model-based coordination strategies for locomotion control.<n>We employ Reinforcement Learning to map proprioceptive observations and commanded velocities to joint-level actions, allowing coordinated locomotor behaviors to emerge.
arXiv Detail & Related papers (2026-03-17T15:43:15Z)
TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards [6.836413501859183]
This paper presents a learning-based fault-tolerant framework for bipedal locomotion.<n>Joint locking, power loss and external disturbances are injected in simulation to learn fault-tolerant locomotion strategies.<n>In addition to transferring the learned policy to the real robot via sim-to-real transfer, an online joint status module is incorporated.
arXiv Detail & Related papers (2026-02-05T12:30:49Z)
FreeAction: Training-Free Techniques for Enhanced Fidelity of Trajectory-to-Video Generation [50.39748673817223]
We introduce two training-free, inference-time techniques that fully exploit explicit action parameters in robot video generation.<n>First, action-scaled classifier-free guidance dynamically modulates guidance strength in proportion to action magnitude, enhancing controllability over motion intensity.<n>Second, action-scaled noise truncation adjusts the distribution of initially sampled noise to better align with the desired motion dynamics.
arXiv Detail & Related papers (2025-09-29T03:30:40Z)
Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
arXiv Detail & Related papers (2025-09-11T17:59:07Z)
Learning Humanoid Standing-up Control across Diverse Postures [27.79222176982376]
Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems.<n>We present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch.<n>Our experimental results demonstrate that the controllers achieve smooth, stable, and robust standing-up motions across a wide range of laboratory and outdoor environments.
arXiv Detail & Related papers (2025-02-12T13:10:09Z)
Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
arXiv Detail & Related papers (2023-04-19T17:37:54Z)
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples [38.81854337592694]
This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. We present experimental results showing that even in a model-free setup, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot.
arXiv Detail & Related papers (2021-10-28T10:14:47Z)
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
arXiv Detail & Related papers (2021-03-26T07:14:01Z)
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
arXiv Detail & Related papers (2020-08-18T08:05:15Z)
Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
arXiv Detail & Related papers (2020-04-02T02:56:16Z)

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