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- YC application open signal
Treat this as a signal. https://ycombinator.com/apply
Y Combinator · X ·
- US Capital Values Zero-Revenue PI at $20 Billion, Chinese Capital Pushes Robot Shipments: Two Sets of Embodied Intelligence Pricing Logic
Physical Intelligence, a robotics AI company founded just over two years ago with no publicly disclosed revenue, is in talks to raise at least $1 billion at a target valuation of about $20 billion, with existing investors Avenir and Lux Capital likely to lead the round.
钛媒体 ·
- GenSight: A Unified Embodied World Model for Video Generation and 4D Perception
GenSight is a unified embodied world model that integrates future video generation and dense perception into a single pretrained video diffusion Transformer. It uses a unified spatiotemporal representation covering video, depth, embodiment segmentation, and 2D optical flow relative to the first frame; built on a shared DiT backbone, it predicts each modality within the same architecture via lightweight task modulation, and jointly optimizes them in a unified velocity prediction space. At inference, it supports multi-step future video generation with aligned dense prediction, as well as single-step dense perception from an observed video. Training data covers a variety of robot embodiments and human hand interaction.
arXiv cs.RO ·
- Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
The paper proposes a Transformer-based architecture with shared modular recurrence for universal morphology robot control and evaluates its generalization performance on a set of MuJoCo robots. The authors note that contextual features are often only partially available but can be recovered through modular interactions, improving multi-robot control and generalization to contexts unseen during training. Results show substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies across four environments.
arXiv cs.RO ·
- MUSBRIC: LEGO-Compatible Pneumatic Muscle Modules for Reconfigurable Musculoskeletal Robots
The authors present MUSBRIC, a modular hardware platform and accompanying MuJoCo simulator for reconfigurable musculoskeletal robots, which uses LEGO-compatible endcaps to turn fiber-reinforced extensile pneumatic artificial muscles into reusable, plug-and-play muscle modules. An elbow-joint prototype validated load-dependent flexion and extension, with pulley-guided routing enabling reversible motion and a mechanical stop stabilizing the straight posture; a robotic hand demonstrated compliant grasping and multi-joint construction, 15 leg configurations completed matched sweeps in hardware and simulation, and a quadruped assembled from four legs achieved walking. The hardware design and simulation code will be open-sourced.
arXiv cs.RO ·
- LOGIV: A Task-Graph Framework for Verified Execution and Causal Local Repair in Long-Horizon Robot Tasks
LOGIV is a task-graph framework for long-horizon robot tasks that connects symbolic planning, observation-based verification, and embodied execution. During execution it checks the physical validity of condition-support and resource-ordering edges, and after a failure it uses dependency tracing to determine which parts must be restored.
arXiv cs.RO ·
- OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation
OpenFlyV2 is a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within a single model. The authors built a simulation dataset of approximately 70,000 navigation trajectories and 60,000 vision-language instruction-response pairs. On an NVIDIA Jetson Orin NX, the model runs fully onboard and generates action chunks of 12 waypoints each at 10 Hz.
arXiv cs.RO ·
- Actuator-Aware Observer Inputs for Cascaded ADRC of Quadrotors
The paper proposes actuator-aware (AA) observer inputs: the altitude and attitude observers use limited virtual inputs filtered by an assumed rotor model and aligned with the measurement delay, while the horizontal observers use acceleration reconstructed from measured attitude.
arXiv cs.RO ·
- SimRetract: Zero-Shot Sim-to-Real Transfer for Retractor Take-Over in Open Surgery
SimRetract proposes a policy trained only on simulated demonstrations that lets a robotic arm take a retractor from a surgeon's hand and maintain retraction at handoff, achieving zero-shot sim-to-real transfer for surgeon-robot collaboration in open surgery.
arXiv cs.RO ·
- TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation
The research team released TacClaw-Infinity, a large-scale structured Vision-Tactile-Language-Action (VTLA) dataset for contact-rich manipulation, spanning over 10,000 hours, 1 million episodes, and 1 billion frames across more than 1,000 task types.
This dataset provides a collection interface and quality control pipeline for ten-thousand-hour-scale tactile manipulation data, and its cross-embodiment transfer validation approach can serve as a reference.
arXiv cs.RO ·
- I3L: An Iterative Interactive Imitation Learning System for Whole-Body Mobile Manipulation
The research team proposes I3L, a hardware and software system that combines whole-body correction with iterative policy refinement to mitigate the out-of-distribution degradation of offline imitation learning policies in long-horizon whole-body mobile manipulation.
arXiv cs.RO ·
- Detect and Suppress: A Mechanistic Defense Against Adversarial Patches on VLA Models
The authors use a sparse autoencoder (SAE) to analyze the internal representations of a VLA model, identify a feature whose activation strongly correlates with the presence of adversarial patches, and suppress that feature at inference time only when a linear probe detects an attack, thereby improving robustness without fine-tuning the VLA. In adversarial patch attack tests on LIBERO-10, conditional intervention improved the success rate under intermittent attacks, whereas continuously applying the same intervention substantially degraded policy performance.
arXiv cs.RO ·
- D4orm diffusion-denoising framework extended into a family of multi-robot trajectory planners
Based on the dynamics-aware diffusion-denoising framework D4orm, this paper builds a family of multi-robot trajectory planners for different operational requirements, including a decoupled planner, an online receding-horizon planner with feedback control, and a distributed planner. Evaluations with differential-drive and holonomic robots in 2D and 3D environments show that D4orm-based methods find high-quality solutions faster and more reliably than sampling-based optimization methods such as MPPI and a learning-based diffusion model method.
arXiv cs.RO ·
- CDIP: Compressed Delayed-Information Projection for Underwater Navigation Under Acoustic Measurement Latency
The paper proposes compressed delayed-information projection (CDIP), which handles delayed-arrival acoustic position measurements in a 15-dimensional error-state Kalman filter (ESKF), retaining source-associated IMU snapshots and the current-to-snapshot cross-covariances, so delayed corrections can be projected without repeating the filter from the start.
arXiv cs.RO ·
- Study: Learning and Transferring Closed-Loop Robot Software
The research treats complete closed-loop robot policy implementations as reusable execution experience: a coding agent generates policy code from a few successful demonstrations and iteratively improves it using simulation feedback. Across four source tasks in RoboCasa, iterative optimization raised the success rate from 28.3% with initially generated code to 64.2% with validation-selected code, and the learning approach was validated on a physical Unitree G1.
arXiv cs.RO ·
- Extending the Franka Emika Panda's Life: A ROS 2 Software Stack with a Reliable Position Interface
This paper releases an open-source ROS 2 software stack that restores ROS 2 support for the Franka Emika Panda and resolves the long-standing unreliability of its external position control interface. The authors' analysis attributes vibrations and protective stops to external control loop timing and sampling jitter rather than to limitations of the robot itself, and on that basis they propose an asynchronous hardware interface that decouples real-time communication from the ROS 2 control loop, a rate-matching mechanism for slower command sources, and a position-domain reference generation strategy.
arXiv cs.RO ·
- UniCross: A Unified Cross-Skill Dexterous Manipulation Synthesis Framework
UniCross proposes a unified framework that models four dexterous manipulation skills—grasping, relocation, in-hand rotation, and in-hand translation—as the same relational-target formulation, and distills a single policy that does not depend on skill identifiers. The policy performs well on each individual skill, generalizes to unseen objects, and supports long-horizon skill chaining and execution of relational targets unseen during training; the framework also applies to different hand morphologies.
arXiv cs.RO ·
- DR-LfD: Decomposing and Reorganizing Demonstrations with Atomic Skills and Visuomotor Policies for Bimanual Manipulation Planning
The authors propose DR-LfD, a framework that integrates visuomotor policies into a TAMP-gated decision-making system, decomposes human demonstrations into atomic skills based on contact relationships, and then reproduces and reorganizes them using visuomotor policies or object-centric primitives. This method shifts the demonstration data requirement from growing exponentially with the skill sequence to growing with the number of skill types, and on two DexMimicGen tasks it outperforms a diffusion imitation policy trained on 1,000 demonstrations while using only 100 demonstrations. The paper has been accepted by T-RO.
arXiv cs.RO ·
- Using a Robotic Arm as an Attitude Actuator: Propellant-Free Attitude Maneuver Planning for Free-Floating Spacecraft
This research uses manipulator motion for propellant-free attitude control of free-floating spacecraft, modeling it as a trajectory optimization problem with joint and collision-avoidance constraints and solving it with an interior-point method. Across a range of spacecraft-manipulator systems, the method computed rest-to-rest slew trajectories of up to 90° and detumble trajectories with a 40% reduction in angular velocity. The authors also directly compare the manipulator's control authority with that of reaction wheel arrays via momentum and torque envelopes, noting that manipulators could serve as a redundant or even primary attitude control system, with particular promise for in-space assembly and manufacturing involving the capture of high relative-mass payloads.
arXiv cs.RO ·
- SPARK: Sequential Planning via Anchored Robotic Keypoints
The authors propose SPARK (Sequential Planning via Anchored Robotic Keypoints), which describes tasks as a symbolic behavior tree, anchoring to the current 3D observation only when each action is invoked; it verifies the plan at runtime and re-anchors objects on local failure, requesting a new plan only if this step fails.
arXiv cs.RO ·
- VEGA: Training Navigation VLAs with In-the-Wild Egocentric Video and Geometric Trajectory Supervision
VEGA proposes a method for training navigation Vision-Language-Action (VLA) models from unlabeled egocentric navigation videos. It reconstructs local scene geometry from monocular video, samples text, image, and spatial waypoint goals, and generates obstacle-avoidance trajectories, then uses these trajectories to train a flow-matching VLA navigation policy; geometry is used only during training.
arXiv cs.RO ·
- GES: Accelerating Morphology-Control Co-Design for Buoyancy-Assisted Robots with Gaussian Evolutionary Specialists
This study proposes Gaussian Evolutionary Specialists (GES), a reinforcement learning-based co-design framework. By maintaining a population of specialist policies whose design-space territories evolve according to their control performance, it mitigates behavioral diversity collapse, in which a single shared policy converges to the same control strategy across different morphologies.
arXiv cs.RO ·
- NDPP-Grasp: Non-Differentiable Physical Plausibility Constraints Guide Task-Oriented Dexterous Grasp Generation
NDPP-Grasp proposes injecting physical plausibility constraints directly into the denoising process of a task-aligned grasp diffusion model, so that guidance can be applied throughout generation even when the constraints are non-differentiable. Existing diffusion methods first train a grasp diffusion model for task alignment and then apply physical plausibility correction after generation, leaving the generation trajectory itself unguided by physical constraints. The work was accepted to NeurIPS 2026.
arXiv cs.RO ·
- TacO: A Benchmark Evaluation of Tactile Sensors for Object Manipulation
The authors propose the TacO framework to systematically evaluate tactile sensors based on manipulation policy performance, covering four modalities: visual, acoustic, magnetic, and resistive. They train separate policies for three tasks—pick-and-place with unknown mass, object reorientation, and plug insertion—and analyze how sensor properties such as spatial resolution, shear sensing, tactile representation, and material friction affect task performance. The results show that the value of tactile information depends on the sensor modality, material properties, and specific manipulation task, rather than being universally beneficial; the sensors, code, data, and hardware designs will be made public on the project website.
arXiv cs.RO ·
- AlphaAdj: A Robot Navigation Framework That Adaptively Tunes Control Barrier Functions with Vision-Language Models
The authors propose AlphaAdj, a vision-to-control navigation framework that uses first-person RGB observations and a vision-language model (VLM) to adjust the conservativeness of control barrier functions (CBFs) according to visual semantic context. The VLM outputs a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds how aggressive the behavior can be. To cope with VLM latency, inference and the control loop run asynchronously, and the semantic signal is taken directly from token-level output probabilities rather than from full autoregressive text generation.
arXiv cs.RO ·
- Specification-aware action distribution optimization framework for robotics foundation models
The paper proposes a specification-aware action distribution optimization framework that enforces a class of Signal Temporal Logic (STL) constraints at execution time without modifying the parameters of a pretrained robotics foundation model. At each decision step, the method reasons over the remaining horizon via forward dynamics propagation to compute a minimally modified action distribution that satisfies a hard STL feasibility constraint, and it is validated in simulation with state-of-the-art robotics foundation models across multiple environments and complex specifications.
arXiv cs.RO ·
- FlowTouch: A View-Invariant Visuo-Tactile Prediction Model
FlowTouch is a view-invariant visuo-tactile prediction model that uses an object's local 3D mesh to encode information for predicting tactile patterns, enabling tactile feedback even before the object is touched. The method combines scene reconstruction with a Flow Matching-based image generation model. Results show that it narrows the sim-to-real gap and generalizes to new sensor instances, and the generated tactile images can also be used for downstream grasp stability prediction. Code, dataset, and videos are publicly available.
arXiv cs.RO ·
- π-StepNFT: A Step-wise Negative-aware Online Reinforcement Learning Framework for Streaming VLAs
The authors propose π-StepNFT, an online reinforcement learning framework for streaming VLAs that treats transitions in SDE sampling as supervision units and uses variance-normalized transition errors to distinguish positive and negative velocity branches, without computing the final-action likelihood.
arXiv cs.RO ·
- CHOP: Counterfactual human preference labels improve obstacle avoidance in visuomotor navigation policies
The authors propose CHOP, which fine-tunes visuomotor navigation policies with counterfactual human preference labels to align them with human intuition about safety and obstacle avoidance. On the SCAND dataset, multiple advanced models fine-tuned with CHOP reduced near-collision events by an average of 49.7%, decreased deviation from human-preferred trajectories by 45.0%, and increased average obstacle clearance by 19.8%.
arXiv cs.RO ·
- WildOS: An Open-Vocabulary Object Search System for the Wild
WildOS is a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning for complex outdoor environments without prior maps and with limited depth sensing. The system builds a sparse navigation graph to maintain spatial memory and uses a foundation-model-based vision module, ExploRFM, to score the graph's frontier nodes, simultaneously predicting traversability, visual frontiers, and object similarity to enable real-time onboard semantic navigation.
arXiv cs.RO ·
- EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Framework for Spatially Generalizable Contact-Rich Tasks
EquiContact proposes a hierarchical framework consisting of a high-level vision planner, Diff-EDF, and a low-level compliant visuomotor policy, G-CompACT, for learning contact-rich manipulation policies that generalize across task configuration spaces.
arXiv cs.RO ·
- CausalWAM: Learning the Causal Sources of Actions in World Action Models
The authors propose CWAM (Causal WAM), which adds sparse routing paths to the action head of a world action model to separate the latent variables that truly drive actions from the nuisance variables that scene prediction depends on, while keeping the world modeling objective unchanged. The paper proves that under a sparsity constraint, the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone, and the true structure is identifiable up to a permutation of the latent variables, without requiring sufficient temporal dependence or assumptions about how unobserved states evolve. Synthetic data and real-robot experiments validate the theory and show improved closed-loop success rate efficiency.
arXiv cs.RO ·
- Sensorium: An open-source multimodal data collection and curation library for embodied surgical AI
The authors released Sensorium, an open-source Python library built on ROS and ROS2 middleware that records, processes, annotates, and curates multimodal surgical data from configurable robot and sensor streams, covering operator-side acquisition, configurable processing, semantic annotation, and episodic dataset generation. The library was evaluated on 1516 recordings of phantom, ex vivo, and in vivo porcine experiments involving multiple surgical tasks and sensing configurations, with preliminary usability feedback reported.
arXiv cs.RO ·
- PGGT-Loc: Feed-Forward Camera Localization with Primitive-Grounded Geometry Transformer
The authors propose PGGT (Primitive-Grounded Geometry Transformer), a network that takes a single query image and a set of planar map primitives as input and directly predicts a 6-DoF camera pose, enabling feed-forward camera localization. The method sets new state-of-the-art localization performance on both dense surface maps and minimal layout models. Code and models are publicly available, and the paper was accepted to NeurIPS 2026.
arXiv cs.RO ·
- DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction
DrawSplat proposes an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. The method first estimates camera poses, sparse geometry, and gravity direction through joint point-line tracking, then infers structural planes from tracked lines using the shared gravity component of the Atlanta World prior and converts them into differentiable regularization terms on Gaussian centers and normals. The framework also combines an anchor-based implicit neural Gaussian representation with adaptive structural sampling, achieving robust tracking, high-fidelity rendering, and geometrically consistent reconstruction across multiple datasets.
arXiv cs.RO ·
- FOCUS: A First-Order Control Method for Observation-Only Imitation Learning Supervised by Optimal Transport
FOCUS combines local optimal transport alignment with short-horizon differentiable actor-critic learning for observation-only imitation learning, requiring neither temporally aligned expert trajectories nor expert-aligned initialization. The method matches agent rollout windows to multiple sampled expert segments using a soft best-of-K objective, with the transport cost providing a differentiable imitation reward and a learned critic bootstrapping value beyond the rollout horizon.
arXiv cs.RO ·
- SACQ: Structure-Aware Codebook Quantization for Ultra-Low-Bit Vision-Language-Action Models
SACQ is an ultra-low-bit weight quantization framework for Vision-Language-Action (VLA) models, combining structure-aware codebook fitting, action-aware mixed-bit allocation, and two-stage post-quantization refinement.
arXiv cs.RO ·
- MotorSense releases first large-scale paired egocentric video and EMG dataset
MotorSense is the first large-scale paired egocentric video and surface electromyography (EMG) dataset, containing synchronized video, bimanual EMG, IMU, and audio across 100 hours of unscripted manipulation. The research uses this data for cross-modal pre-training between video and EMG to learn an action organization shared across visual and motor spaces, and transfers it to tasks including action recognition, 3D hand reconstruction, and 3D contact reconstruction. Across four action-related visual tasks and eight video datasets, this sensorimotor-aligned representation outperforms conventional video self-supervised methods.
arXiv cs.RO ·
- arXiv paper compares perimeter defense performance of centralized vs. decentralized LLM multi-agent teams
An arXiv paper compared the performance of a centralized commander, an auction-based consensus team, and independent LLM agents under five network conditions in a simplified perimeter-defense simulation. The consensus team had a 15% breach rate, the commander 32%, and independent agents 29%.
arXiv cs.RO ·
- cuPIQP: A Differentiable Batched QP GPU Solver for Robot Learning and Control
cuPIQP is a GPU batched differentiable quadratic programming (QP) solver based on a proximal interior-point method, with unified support for dense, sparse, and multistage problems. In benchmarks with up to 16,384 QPs per batch, it solves up to 18.8 times faster than Moreau and 4.26 times faster than eight-core PIQP; in an end-to-end learning task, training is accelerated by 4.6 times over QPAX and 7.8 times over Moreau.
arXiv cs.RO ·