arXiv AI
Jun 26
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
arXiv:2606. 27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks.
Robotics and embodied AI
Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv Machine Learning
Jun 26
Revisiting Action Factorization for Complex Action Spaces
arXiv:2606. 26574v1 Announce Type: new Abstract: Many real-world control problems involve hybrid discrete-continuous action spaces.
arXiv AI
Jun 26
HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
arXiv Machine Learning
Jun 26
Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
arXiv:2606. 26154v1 Announce Type: cross Abstract: Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge.
Hugging Face Trending Papers
Jun 25
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently long-horizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization.
Hugging Face Trending Papers
Jun 25
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation. Existing systems treat these as separate problems: VLM-based planners lack a unified cyber-physical action space, agent frameworks accumulate unbounded context that degrades temporal coherence, and VLA policies execute open-loop without detecting their own failures.
Hugging Face Trending Papers
Jun 25
Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interaction
Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world autonomy, there is a growing, yet largely unmet, need for open vocabulary object detectors that are prompt-free and efficient enough for continuous deployment.
Hugging Face Trending Papers
Jun 25
PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation
Humanoid motion imitation requires not only accurate perception of human kinematics but also faithful reproduction of physical interactions with the environment. However, existing pipelines rely primarily on vision-based motion capture and kinematic imitation, largely ignoring contact dynamics, leading to artifacts such as foot sliding, floor penetration, and unstable behaviors.
Hugging Face Trending Papers
Jun 25
LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes.
arXiv Machine Learning
Jun 25
Bridging Spherical Black-Box Optimizers
arXiv:2606. 25761v1 Announce Type: new Abstract: When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative.
arXiv Machine Learning
Jun 25
Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See
arXiv:2606. 25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving.
arXiv Machine Learning
Jun 25
Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory
arXiv:2606. 25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights.
arXiv Machine Learning
Jun 25
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
arXiv:2505. 23866v2 Announce Type: replace Abstract: Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving.
arXiv Machine Learning
Jun 25
PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution
arXiv:2507. 01695v3 Announce Type: replace Abstract: Deep neural networks (DNNs) are widely used for their ability to model complex patterns across domains such as computer vision, speech recognition, and robotics.
arXiv Machine Learning
Jun 25
Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems
arXiv:2606. 24958v1 Announce Type: new Abstract: Collective behavior arises when locally interacting units produce coordinated global organization, from synchronization in dynamical systems to task-relevant information flow on graphs.
arXiv Machine Learning
Jun 25
Safe Learning Control with Optimality and Stability Guarantees
arXiv:2501. 15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance.
arXiv Machine Learning
Jun 25
Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning
arXiv:2606. 25700v1 Announce Type: new Abstract: When fine-tuning Large Language Models (LLMs), there has been success in minimizing both memory usage and computation with Parameter-Efficient Fine-Tuning (PEFT), like Low Rank Adaptation (LoRA).
arXiv Machine Learning
Jun 25
Solving Markov Decision Processes with Future Information via MPC
arXiv:2606. 24991v1 Announce Type: cross Abstract: Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning.
arXiv Machine Learning
Jun 25
Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures
arXiv:2601. 23147v2 Announce Type: replace Abstract: The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, such as smart grids and microgrids.
arXiv Machine Learning
Jun 25
ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models
arXiv:2606. 25800v1 Announce Type: new Abstract: Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional autoregressive action policies.