The study examines how closely pretrained LeWorldModel (LeWM) reproduces small action changes, measuring action sensitivity with Gram matrices at three perturbation scales across nearly a thousand states from Reacher and OGBench Cube.
The study proposes an interpretability pipeline and finds that injecting task A's activations into the action expert causes pi0.5 to execute task A's motor trajectory in 99.6% of episodes (n=1,968), and that the injected program is bound to absolute workspace coordinates while ignoring the visible scene, offering a mechanistic explanation for the perturbation brittleness reported by benchmarks.
Why it matters: The study uses intervention experiments to provide a mechanistic explanation for the brittleness of VLA policies, helping readers understand why models fail after slight scene changes.
Researchers at the Grainger College of Engineering at the University of Illinois Urbana-Champaign combined reinforcement learning with control theory to propose a contraction-aware reinforcement learning method, CARL, which guarantees robot stability while improving tracking performance by up to 15% compared with existing methods.
ConfigWM proposes a configurable perception mechanism that uses task language descriptions as configuration signals to guide the mapping of visual embeddings into task-specific state spaces, selectively retaining task-relevant information and suppressing distractions. In environments with progressively increasing distraction, existing methods show a marked decline in planning success rate, while ConfigWM remains relatively stable, averaging 22.3% higher than the strongest competing method. Physical-quantity probing (Pearson r +0.365) and distracted-to-clean retrieval (+75.5%) also show consistent improvements.
Li Long, a PhD student at Griffith University, Zhao Qichao, a PhD student at Tsinghua University's Institute for Interdisciplinary Information Sciences, and teams from Fudan University and Tongji University propose Spotter: an embodied model executes continuously and autonomously while a lightweight local model supervises in parallel, and the VLM intervenes to correct errors only when they occur, avoiding the robot waiting for VLM inference at every step.
Slot-JEPA decomposes JEPA's dense patch representations into stable semantic slots and time-varying spatial masks, providing a reusable object-structured interface. Built on this interface, the Transition Memory Predictor (TMP) stores visual transitions as compact masks, retrieves context using slot statistics, and uses unbalanced optimal transport (UOT) to supervise mask prediction and evaluate candidate plans.
DragMesh: Interactive 3D Generation Made Easy, proposed by a Peking University team, has been accepted to IJCV. It targets local interaction on existing 3D assets, recovering the target part's joint type, motion axis, and joint origin through a single drag, and generating a complete, continuous joint motion trajectory without retraining a motion model for each new object.
The study proposes OverSeer, a multi-agent world model that adds auxiliary camera views fixed in space alongside agents' egocentric views, serving as a proxy for a world-centric representation to improve temporal and cross-agent consistency. The auxiliary views are redundant and nearly static, so they can be efficiently compressed with a delta-VAE that encodes only changes relative to a static reference image, reducing the number of tokens required.
A robotics project uses evolutionary search and AI-generated CAD in simulation to let a robot iteratively design and test tools such as grippers to improve task performance; the robot's motions still come from a predefined task controller. The next step is to validate on real hardware using an Arctos robot and a 3D printer, and the author is seeking suggestions for automatically removing gripper fingers from the print bed.
PhysisServe, a physical AI inference system jointly released by Inverse Matrix, vLLM, the Beijing Academy of Artificial Intelligence, and Peking University, completes one chunk every 4.083 milliseconds for the 33B-parameter MiniMax H3 on 200 GPUs, about 245 chunks per second, with equivalent frame throughput approaching 1,000 frames per second and a steady-state output interval shorter than that of the 1.3B-parameter Wan2.1.
An NVIDIA research team proposes the JCA model, which can simultaneously generate video and replayable 3D motion for any given skeleton, and output a camera that registers the motion to the scene. The model keeps 3D motion in a separate model that supports arbitrary skeletons, connects to a pretrained video model through a small number of cross-attention layers, and the motion model first learns skeleton movement from motion-only data.
Urbex is an agentic vision-language framework for city-scale 3D scenes that shifts from static ranking based on pre-generated candidate boxes to active spatial search, progressively narrowing the search area through three tools—landmark query, local zoom-in, and oblique view—and uses GRPO reinforcement learning to optimize tool-invocation strategies.
Estimating empowerment from offline data is useful because it provides an unsupervised objective for learning the relative "value" of each state in reaching diverse outcomes. But making it computable is notoriously difficult. In our new paper, we propose a computable estimator, along with some applications!
CondenseVLA proposes adding a learnable history compression module to the decoding side of VLA policies. It uses learnable queries and multi-head cross-attention to distill the per-frame features cached by the VLM backbone into a fixed number of slot tokens, and applies auxiliary regularization that penalizes overlap among slot attention maps and decorrelates slot embeddings to reduce redundancy.
VLA-Rolling is a lightweight inference framework that reuses action tails through three stages: action-tail inheritance, per-action confidence estimation, and confidence-guided refinement based on renoising and denoising step allocation, while the underlying VLA model remains frozen.
This work presents the first fully polynomial-time approximation scheme (FPTAS) for the multi-robot motion planning problem, for a fixed number k of translating polygonal robots in a polygonal environment, covering the min-sum, min-max, and min-makespan objectives. The algorithm generalizes to polytope environments and polytope robots in any fixed dimension d, and it resolves the open problem raised by Agarwal et al. on an FPTAS for k=2 unit-square and unit-disk robots, while being simpler than existing results. The paper will be published at SODA 2027.
The RoboFake paper proposes using a real humanoid face robot with synchronized lip movements and expressions, combined with generated speech, to create a new type of presentation attack with no pixel editing, and tests three public presentation attack detection (PAD) models and six deepfake detectors.
TAPDreamer proposes an attack method that constructs a fixed local adversarial patch using only a public encoder, requiring no queries to the target policy and transferring across tasks and action architectures. In closed-loop evaluation, a frozen patch covering about 6.5% of the input reduced FastWAM's success rate from 97.7% to 0.0% on 40 LIBERO tasks and from 90.86% to 0.0% on 50 RoboTwin tasks, while a matched random patch still retained 81.5% and 79.2%.
FERPO (Forward Entropy-Regularized Policy Optimization) is an online maximum-entropy reinforcement learning algorithm that improves the policy by differentiating the critic's value rather than its actions, avoiding policy-update problems caused when value predictions are accurate but action derivatives are unreliable.
The EMMA framework enhances vision-language-action (VLA) policies through a generative data engine and training pipeline, addressing the high cost of collecting robot manipulation data across varied appearances. Its core component, DreamTransfer, is based on a diffusion Transformer and generates multi-view-consistent, geometrically plausible manipulation videos, supporting text-driven editing of foreground, background, and lighting while preserving 3D structure.
Gibson Virtual Environment provides a perceptual training environment for embodied agents by virtualizing real spaces, and currently contains more than 1,400 floor spaces from 572 full buildings. Its built-in "Goggles" synthesis mechanism allows trained models to be deployed directly to the real world without additional domain adaptation. The environment subjects agents to physical and spatial constraints, and the paper demonstrates sample perceptual tasks learned within it; the code, dataset, and project website are openly available.
Taskonomy proposes a fully computational method that models 26 2D, 2.5D, 3D, and semantic visual tasks in a latent space, mines first-order and higher-order transfer learning dependencies, and produces a taxonomic map for task transfer learning.
The paper proposes PhysWord, a reasoning-before-acting robotic foundation model that uses atomic action words as shared semantic anchors to align continuous representations of physical dynamics with language. PhysWord predicts upcoming atomic action words and aligns their contextual hidden states with implicit targets derived from future observations, constructing a temporally structured PhysWord space with visual and optional tactile variants.
Researchers developed a layered biomimetic robotic skin that embeds receptor-analog sensors in a compliant soft medium to mimic key structural features of human skin tissue. Using this platform, the study implemented two sensory arrangement schemes, biologically inspired and counterfactual, and quantitatively evaluated them across three passive tactile tasks: weak contact detection, two-point discrimination, and texture identification. Results showed that the arrangement reflecting key features of biologically inspired sensory organization outperformed the other configurations tested.
OmniInteract proposes a whole-body interaction control method for dual humanoid robots. Through Surface Contact-Aware Retargeting, it uses dense human-robot surface correspondences to jointly retarget both humanoids, preserving the interaction contact interfaces and their near-field geometric context.
The paper proposes a human-directed policy search workflow for multi-agent pathfinding in which coding agents iteratively propose, implement, evaluate, diagnose, and refine algorithms in code space, combining simulation feedback with explicit correctness verification.
The paper proposes InteractAgent, a multimodal large language model agent system for long-horizon human-scene interaction that completes planning, execution, observation, and reflection in a closed loop, with a single MLLM serving as both planner and evaluator and a frozen MotionLLM as the executor.
The paper proposes a dynamic camera-IMU extrinsic calibration method based on the Denavit-Hartenberg convention, enabling online estimation of time-varying extrinsics by incorporating a robot kinematic model. A companion two-step initialization strategy efficiently estimates the initial joint angles, providing a reliable starting point for subsequent extrinsic parameter optimization. Simulation experiments show that the framework can effectively accommodate changes in camera-IMU extrinsics while maintaining competitive localization accuracy.
The paper proposes the FIBER framework, which uses the executable latent space of a frozen behavioral foundation model (BFM) as a shared planning representation for heterogeneous interaction skills. Its latent policy projection (LPP) maps independently trained task policies into this representation, enabling low-frequency planning while retaining the BFM's high-frequency state feedback; a single perceptive flow planner is then trained entirely offline. The paper also proposes an adaptive-horizon BFM (AH-BFM) trained with multi-gamma forward-backward learning.
FastVLA proposes a training-free, task-aware, and temporally adaptive visual token compression framework to improve the closed-loop inference efficiency of vision-language-action (VLA) models. It scores tokens via task-to-vision attention and adaptively sets the compression rate based on the uniformity of the importance distribution, calibrated using an inverse Simpson index and an online sliding window.
ForeTac is a predictive contact guidance framework for contact-rich manipulation that uses a represent-predict-evaluate-guide loop: it encodes the current tactile state, predicts future tactile latent trajectories conditioned on candidate action sequences, and evaluates each trajectory with a contact-quality objective learned from successful and failed physical interactions. At inference, gradients of this objective are backpropagated through the tactile predictor to guide action generation before execution, while all model parameters remain frozen.
This dissertation uses offline-trained neural implicit representations to provide the value function from Hamilton-Jacobi (HJ) reachability analysis in real time to optimization-based controllers, addressing the inadequacy of geometric collision checking in unknown dynamic environments.
The paper proposes an end-to-end pipeline that uses an instrument-state logger mounted on the shaft of a standard laparoscopic instrument (combining inertial, time-of-flight, and Hall sensors, with no external camera or tracker) to capture the instrument pose and jaw state during a surgeon's operation, which is then used to train a surgical robot policy and validated on live animals.
The paper proposes a proof-of-concept framework that incorporates a robot's nonlinear dynamics into the design process, enabling multi-gait locomotion to emerge from nonlinear resonances shaped by gravity, inertia, and elasticity. The authors introduce the highly compliant quadruped robot eBert, use new theoretical tools to identify six nonlinear normal modes (NNMs), and validate their existence in simulation and hardware. Using black-box optimization to determine step length, simulations show that each NNM naturally develops into a distinct gait with different speeds, and most of these largely transfer to the physical hardware.
This paper proposes enforcing a control barrier function at the torque level via a quadratic program in the allocation null space, keeping the control authority of an overactuated multirotor forward-invariant. In simulations of a hexarotor and a fully actuated octorotor, across 300 randomized tasks, the greedy maximization scheme violated the certified lower bound in up to 51% of authority-scarce tasks, whereas the proposed filter never left the certified set, and in the hardest torque-reversal scenario the torque error was 79 times smaller; the robust variant maintained the lower bound on all 150 perturbed objects.
The paper proposes WorldScape Policy 2.0, a controllable World Action Model (WAM) with reasoning-augmented long short-term memory. Its causal short-term visual memory supplies recent observations as DiT prefill to preserve local interaction dynamics, while its long-term event memory organizes historical VLM outputs into global-history, local-active, and event-boundary representations for progress-aware retrieval and to generate implicit subgoal conditions.
The paper proposes a modular method that autonomously collects data in local segments of contact-rich tasks and uses offline reinforcement learning to train local policies, which are then integrated with traditional motion planning to complete long-horizon assembly tasks. Across four real-world tasks, using only 2-2.5 hours of autonomous data collection, the method achieved an average success rate of 96% on long-horizon tasks, compared with 55% for a planning-only baseline and 37% for the strongest end-to-end baseline; the local policies also reduced applied force by 49%. Performance remained at 90% in scene-level out-of-distribution scenarios, with more than 1,500 real-world evaluation rollouts completed in total.
PAKE proposes a whole-body loco-manipulation control framework that decomposes the problem into partial reference motion generation and low-level imitation control. It introduces a Kinematic Normalizing Flow (KNF) model trained on a large-scale motion generation dataset to generate diverse partial motion references; a high-level controller navigates the KNF latent space to exploit redundant solutions, while a low-level controller learns dynamically consistent motion execution.
The paper proposes KITE (Kinematic Interaction Transfer across Embodiments), which splits manipulation into embodiment-agnostic interaction intent prediction and embodiment-specific kinematic decoding, connected through an interaction intent representation based on contact patterns.
ZeroDex is a zero-shot long-horizon dexterous manipulation framework that grounds language instructions into executable 3D task plans from calibrated multi-view RGB images. It leverages the 2D grounding ability VLMs already possess and lifts it to the metric 3D quantities manipulation requires (contact points, placement targets, tool-motion anchors) through multi-view fusion; execution is driven by these 3D groundings rather than a learned policy, requiring no demonstrations or post-training for a specific task or embodiment.