News & Research

arXiv:2611.00283v1 Announce Type: new Abstract: Held-out validation loss is the standard offline signal for choosing checkpoints and comparing models, but its relation to closed-loop success on a real robot is rarely measured. We measure it for ACT, Diffusion Policy, SmolVLA and $\pi_{0.5}$ (action expert only), trained on nested subsets of 12 to 200 demonstrations of a pick-and-place task on an SO-101 arm. We score 82 checkpoints on 25 held-out demonstrations, with each policy's training loss and with the error of the actions it outputs, and compare the scores with 930 bench rollouts. We also score all 700 intermediate checkpoints of 70 training runs, and recover the ball positions of every training, held-out and bench episode from the front camera. Within each policy, hold-out action error correlates with bench success (Spearman $\rho$ from -0.23 to -0.88), but no more strongly than the number of training episodes alone, and not at all among seeds trained on the same data. Across pol

arXiv:2611.00237v1 Announce Type: new Abstract: Uncontrolled hemorrhage remains a leading cause of preventable trauma mortality. Timely intervention is not always available, making autonomous systems that can aid in bleeding control a promising alternative. Given the safety-critical nature of the task, supervised autonomy, in which the system executes the technical steps while a human retains oversight, represents a necessary intermediate step toward this goal. In this work, we present a supervised autonomous bimanual robotic framework for injury cauterization (eletrosurgical coagulation) in low-grade splenic trauma. The system integrates real-time bleeding segmentation, markerless tool keypoint tracking, and deep tool-tissue contact detection to coordinate two robotic arms for synchronized suction and cauterization. Evaluated on a novel, cannulated ex vivo splenic hemorrhage model, the system achieved an average bleeding surface cauterization coverage of 82.9% across seven distinct in

arXiv:2611.00193v1 Announce Type: new Abstract: Humanoid controllers have acquired increasingly diverse motor skills, yet translating these capabilities into precise and stable whole-body manipulation remains challenging. Manipulation tasks are sensitive to execution deviations -- even small errors or unintended steps can cause failure. Introducing Anchor 1, a whole-body controller for precise and stable whole-body manipulation, together with a benchmark spanning diverse coordination demands. Anchor 1 adopts calibrated sparse motion references to preserve task-relevant human motion structure without online joint-space retargeting, leaving kinematic freedom for precise control. To guide this freedom towards stable, physically plausible whole-body coordination, we shape the training distribution through offline curation of robot motions paired with these calibrated sparse references. We further introduce explicit error feedback decoupled from motion encoding, improving control precision

arXiv:2611.00180v1 Announce Type: new Abstract: Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between tasks and during execution. We introduce LIFE (Lifelong Intelligence from Experience), a framework in which a robot observes continuously, remembers selectively, and turns experience into new capabilities. RoboBeats, a family of lightweight streaming gates, tracks conditions set by the agent and discovers new events, deciding when an observation warrants a response or a memory write. Selected observations enter Plastic Memory, a flat store of images and structured records that keeps the source evidence, so new fields and indexes can be added when later questions need them. When existing tools fall short, Explore-Distill-Mount (EDM) turns solutions found during problem solving into validated, reusable tools and skills. Under matched protocols, RoboBeats substantially improves condition tracking over its base models and, at the same

arXiv:2611.00158v1 Announce Type: new Abstract: Robots powered by foundation models are increasingly able to interpret open-ended language, recognize unfamiliar objects, and operate across diverse environments. Yet broader capability does not establish that a robot has understood a request, that its actions will produce the intended effects, or that its internal representation still corresponds to reality. Classical symbolic planning provides explicit structure for composition and verification, but traditionally depends on hand-engineered skills and representations. Learned policies can broaden the robot's repertoire, but provide weak guarantees about what will happen and why. This thesis argues that scaling trusted autonomy requires a neurosymbolic approach that unifies the generality of learned models with symbolic structure for robust robot behavior. It explores three complementary directions. First, LIMP uses foundation models and spatial reasoning to ground natural-language instru

arXiv:2611.00155v1 Announce Type: new Abstract: Post-incident analysis of a robot foundation model poses a singular causal question: which internal components caused this failure, and to what degree? Mechanistic interpretability (MI), however, establishes general causal claims by decoding the concepts a policy has learned, yet knowing which concepts are encoded is insufficient for identifying the actual cause of a specific incident. In this position paper, we analyze this concept-malfunction gap by posing fault isolation in the framework of actual causality. Specifically, we formalize a fault-isolation query as a tuple of intervened internal components, counterfactual values, contingencies, and read-outs, and lay out conditions for it to be well-specified. Through this lens, we identify shortcomings of current MI approaches: they assume fixed contingencies and read out only immediate effects, letting backups obscure actual causes. We demonstrate on a toy example that these shortcomings

arXiv:2605.21976v2 Announce Type: replace Abstract: Vision-based learning from demonstrations has achieved remarkable success in enabling robots to perform manipulation tasks and high-level semantic reasoning, yet it remains insufficient for complex, contact-rich manipulation. While there is broad agreement that tactile sensing improves manipulation, there is no empirical guidance on which tactile sensors are best suited for which manipulation tasks. In this paper, we provide a systematic, task-driven evaluation of tactile sensors for robot manipulation and propose a framework for selecting and evaluating sensors based on manipulation policy performance. Separate manipulation policies are trained for tactile sensors of four distinct modalities: visual, acoustic, magnetic, and resistive, across three tasks: pick-and-place with unknown mass, object reorientation, and plug insertion. For each task, we analyze how sensor properties, including spatial resolution, shear sensing, tactile repr

arXiv:2603.21142v2 Announce Type: replace Abstract: Robots operating in dynamic, unstructured environments must maintain safety without becoming unnecessarily conservative. While control barrier functions (CBFs) provide a principled mechanism for collision avoidance, their behavior is typically determined from geometric state alone and therefore cannot distinguish between semantically different situations with similar physical configurations. We present AlphaAdj, a vision-to-control navigation framework that uses egocentric RGB observations and a vision-language model (VLM) to adapt CBF conservativeness according to visual semantic context. The VLM produces a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds admissible aggressiveness. To support real-time operation despite VLM latency, inference runs asynchronously with the control loop, and the semantic signal is extracted directly from token-level out

arXiv:2603.17969v2 Announce Type: replace Abstract: Robotics foundation models have demonstrated strong capabilities in executing natural language instructions across diverse tasks and environments. However, they remain largely data-driven and lack formal guarantees on constraint satisfaction during deployment. In practice, robots often need to comply with operational constraints involving rich spatio-temporal requirements such as time-bounded goal visits, sequential objectives, and persistent safety conditions. In this work, we propose a specification-aware action distribution optimization framework that enforces a broad class of Signal Temporal Logic (STL) constraints during execution of a pretrained robotics foundation model without modifying its parameters. At each decision step, the method computes a minimally modified action distribution that satisfies a hard STL feasibility constraint by reasoning over the remaining horizon using forward dynamics propagation. We validate the pro

arXiv:2603.08255v2 Announce Type: replace Abstract: Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched. This precludes their use during planning and the initial execution phase of a task. Predicting tactile information from visual information can bridge this gap. A common approach is to learn a direct mapping from camera images to the output of vision-based tactile sensors. However, the resulting model will depend strongly on the specific setup and on how well the camera can capture the area where an object is touched. In this work, we introduce FlowTouch, a novel model for view-invariant visuo-tactile prediction. Our key idea is to use an object's local 3D mesh to encode rich information for predicting tactile patterns w

arXiv:2603.02004v2 Announce Type: replace Abstract: Visuomotor navigation policies have shown strong perception-action coupling for embodied agents, yet they often struggle with safe navigation and dynamic obstacle avoidance in complex real-world environments. We introduce CHOP, a novel approach that leverages Counterfactual Human Preference Labels to align visuomotor navigation policies towards human intuition of safety and obstacle avoidance in navigation. In CHOP, for each visual observation, the robot's executed trajectory is included among a set of counterfactual navigation trajectories: alternative trajectories the robot could have followed under identical conditions. Human annotators provide pairwise preference labels over these trajectories based on anticipated outcomes such as collision risk and path efficiency. These aggregated preferences are then used to fine-tune visuomotor navigation policies, aligning their behavior with human preferences in navigation. Experiments on th

arXiv:2602.19308v2 Announce Type: replace Abstract: Autonomous navigation in complex, unstructured outdoor environments requires robots to operate over long ranges without prior maps and limited depth sensing. In such settings, relying solely on geometric frontiers for exploration is often insufficient. In such settings, the ability to reason semantically about where to go and what is safe to traverse is crucial for robust, efficient exploration. This work presents WildOS, a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning. WildOS builds a sparse navigation graph to maintain spatial memory, while utilizing a foundation-model-based vision module, ExploRFM, to score frontier nodes of the graph. ExploRFM simultaneously predicts traversability, visual frontiers, and object similarity in image space, enabling real-time, onboard semantic navigation tasks. The resulting vision-scored graph enables the robot to

arXiv:2507.10961v5 Announce Type: replace Abstract: This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks, which generalize spatially across task configurations. We propose EquiContact, a novel hierarchical framework composed of a high-level vision planner, such as a Diffusion Equivariant Descriptor Field (Diff-EDF) and a low-level compliant visuomotor policy, such as a Geometric Compliant Action Chunking Transformer (G-CompACT). G-CompACT, operating using localized observations from wrist-mounted RGB images, end-effector pose feedback in the form of a geometrically consistent error vector (GCEV) and force-torque readings, produces actions defined on the end-effector frame. We show that the entire EquiContact pipeline is $\SE$-equivariant under equivariant reference-frame estimation and left-invariant visual features, from perception to force control. Once the robot learns to execute a contact-rich task, such as inserting a peg-i

arXiv:2610.41946v1 Announce Type: cross Abstract: Embodied surgical AI requires reliable multimodal data, yet surgical data collection remains costly, difficult to repeat, and strongly dependent on the hardware setup. We present Sensorium, an open-source Python library for recording, processing, annotating, and curating multimodal surgical data from configurable robot and sensor streams using the Robot Operating System (ROS) and ROS2 middleware. The library combines operator-facing data acquisition, configurable processing, semantic annotation, and episodic dataset generation. We evaluate Sensorium across 1516 recordings of phantom, ex vivo, and in vivo porcine experiments involving multiple surgical tasks and sensing configurations, and report preliminary usability feedback.

arXiv:2610.41602v1 Announce Type: cross Abstract: RGB-D SLAM requires accurate online tracking, high-quality rendering, and geometrically consistent reconstruction. Existing Gaussian SLAM methods mainly optimize Gaussian primitives with photometric, depth, or local frame-to-model constraints, which are often insufficient in indoor scenes with weak textures, large planar structures, noisy depth, and long trajectories. The resulting maps may suffer from pose drift, surface thickness artifacts, noisy normals, and blurred structural boundaries. We propose DrawSplat, an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. DrawSplat follows a drawing-like process: explicit point-line geometry first sketches the structural layout, and implicit neural Gaussians then fill in high-fidelity appearance and continuous geometric details. It estimates camera poses, sparse geometry, and gravity direction through point-line joint tracking, infers structural planes from track