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.
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.
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arXiv Machine Learning
Jun 11
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
arXiv:2606. 11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy.
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 26
EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning
arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.
arXiv AI
Jul 9
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
arXiv:2506. 08630v3 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency.
arXiv AI
Jul 2
ASPIRE: Agentic /Skills Discovery for Robotics
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
arXiv AI
Jul 14
ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory
arXiv:2607. 10350v1 Announce Type: new Abstract: Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution.
arXiv Machine Learning
Jul 28
MEMENTO: Memory-Guided Memetic Code-as-Policy Evolution
arXiv:2607. 22832v1 Announce Type: new Abstract: Long-horizon embodied tasks require policies that execute many dependent actions before task success can be observed.
arXiv Machine Learning
Jun 19
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
arXiv:2606. 19656v1 Announce Type: cross Abstract: A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience.
arXiv AI
Jun 30
Demonstration-Free Robotic Control via LLM Agents
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
arXiv AI
Jun 9
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
arXiv:2606. 08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms.
arXiv Machine Learning
23h ago
Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
arXiv:2608. 17310v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning.