PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation

Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies. However, because they are trained primarily to fit behavioural demonstration data, they do not explicitly enforce fundamental physical principles such as rigid-body dynamics or contact constraints. This exposes a critical physics gap: standard temporal smoothing applied on top of single-step or chunked VLAs trades trajectory quality for added failures that short-term memory cannot resolve. To bridge this gap, we introduce PhysVLA (Physics-VLA), a plug-and-play, inference-time framework designed to wrap any frozen VLA backbone without retraining, fine-tuning, or weight access, with less than 1 ms of overhead per control step. PhysVLA intercepts the predicted control action, captures only the simulator or system state, and applies a dual-layered correction: (i) a phase-aware finite-state machine that structures discrete task segments (approach, grasp, transport, and place), and (ii) a selective Euler-Lagrange gate that activates only when a dynamics oracle detects kinodynamic inconsistency. Evaluated across OpenVLA, OpenVLA-OFT, Force-VLA, and Generalist-VLA on LIBERO-Spatial with a 7-DoF Franka Panda, the framework delivers absolute success rate increases of up to 17% and stability increases of up to 19% with no per-task regressions, improves trajectory efficiency by up to 15% across all four backbones, and shows up to a 10x improvement in trajectory jerk robustness on a Robosuite Lift cross-simulator sweep. We further validate the framework on a real Agilex Piper arm with a pick-and-place task, confirming that PhysVLA transfers to physical hardware without retraining, with success-rate improvements of up to 50%, establishing physical awareness as a composable, backbone-agnostic runtime module.

Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective

Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning, yet their spatial reasoning abilities remain limited and underexplored, largely constrained to symbolic and sequential processing. In contrast, human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments. Therefore, bridging this gap is critical for advancing Agentic Spatial Intelligence toward better interaction with the physical 3D world. To this end, we first start from scrutinizing the spatial neural models as studied in computational neuroscience, and accordingly introduce a novel computational framework grounded in neuroscience principles. This framework maps core biological functions to six essential computation modules: bio-inspired multimodal sensing, multi-sensory integration, egocentric-allocentric conversion, an artificial cognitive map, spatial memory, and spatial reasoning. Together, these modules form a perspective landscape for agentic spatial reasoning capability across both virtual and physical environments. On top, we conduct a framework-guided analysis of recent methods, evaluating their relevance to each module and identifying critical gaps that hinder the development of more neuroscience-grounded spatial reasoning modules. We further examine emerging benchmarks and datasets and explore potential application domains ranging from virtual to embodied systems, such as robotics. Finally, we outline potential research directions, emphasizing the promising roadmap that can generalize spatial reasoning across dynamic or unstructured environments. We hope this work will benefit the research community with a neuroscience-grounded perspective and a structured pathway. Our project page can be found at Github.