arXiv Machine Learning
Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization
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arXiv AI
Jun 2
Shape Your Body: Value Gradients for Multi-Embodiment Robot Design
arXiv:2606. 00702v1 Announce Type: cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design.
arXiv Machine Learning
Sep 18
GLAMDRING: Gait Learning And Morphology co-Design via Reinforcement LearnING of CPGs
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 Machine Learning
Jun 24
Debate2Create: Robot Co-design via Multi-Agent LLM Debate
arXiv:2510. 25850v3 Announce Type: replace-cross Abstract: We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in physics-based evaluation.
arXiv AI
Jun 12
From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence
arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.
arXiv AI
Jun 8
CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space
arXiv:2601. 05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI.
arXiv Machine Learning
Sep 10
Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
arXiv:2607.12590v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, h...