Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap. The chasm of simulation to deployment in hardware lies in the delay of the actuator reaching the commanded position.
arXiv:2607. 26434v2 Announce Type: cross Abstract: Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap.
arXiv:2509. 06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring millions of interactions with simulated environments to achieve stable control.
GLAMDRING is a framework that jointly designs a quadruped robot’s morphology and its gait controller using reinforcement learning of Hopf-oscillator Central Pattern Generators (CPGs). Given specifications such as forward‑velocity bounds, actuator power budgets, an actuator library, and payload requirements, the system returns an optimized robot design and a corresponding gait policy, ranking designs by objectives like maximum speed, minimum Cost of Transport, or maximum payload margin. Experiments demonstrate that co‑designing body and gait is essential for meeting locomotion constraints, that actuator feasibility determines payload capacity, and that natural animal gaits emerge from the design process, with a real‑world demonstration confirming the approach’s effectiveness.
arXiv:2607. 00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpretability of learned policies and lack explicit control over gait behaviors.
arXiv:2606. 20031v1 Announce Type: cross Abstract: Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conventional search- and rule-based methods, which typically suffer from high computational complexity and long decision latency.
arXiv:2608. 07328v1 Announce Type: cross Abstract: Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility.
arXiv:2608. 12063v1 Announce Type: cross Abstract: Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping.
The paper introduces the λ-hold controller, a minimal-task-reward approach inspired by the equilibrium-point hypothesis, to train a muscle-actuated skeletal model for human-like sprinting. By fixing each muscle’s EP threshold length λ over gait-phase intervals, the controller dramatically reduces the action space and the frequency of policy queries, enabling efficient exploration and learning within an hour of training. This method demonstrates that physiologically grounded control can produce realistic human motion in predictive musculoskeletal simulations.
arXiv:2609.07111v1 Announce Type: cross Abstract: Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. De...
Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely bottlenecked by the slow, manual process of dense reward shaping. To bypass this limitation, we leverage Sample-based Model Predictive Control (SMPC) entirely in simulation as an automated, rapidly tunable expert to generate massive offline datasets.
arXiv:2609.17042v1 Announce Type: new Abstract: Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented...
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process.