Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.
arXiv:2610.01746v1 Announce Type: cross Abstract: Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures off...
arXiv:2610. 01834v1 Announce Type: new Abstract: Judgment models such as \jev{} return, in a single call and without reasoning text, a probability for each described option.
arXiv:2610.02000v1 Announce Type: new Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...
arXiv:2609.39995v2 Announce Type: new Abstract: Diffusion planners exhibit strong capabilities in generating multimodal trajectories. However, existing methods primarily rely on expert demonstrations...
arXiv:2609.36416v2 Announce Type: replace-cross Abstract: Robots operating in real-world environments must often execute complex, multi-step bimanual tasks over long horizons rather than single, isol...
arXiv:2609.40245v2 Announce Type: cross Abstract: Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-...
Screw Attention introduces a transformer layer that treats the relation between two bodies as a spatial transform rather than a graph edge, enabling each token to represent a body with a pose and its relative pose or joint screw. This design ensures equivariance to independent frame changes and allows a single layer to capture rigid‑body velocity recursion. Experiments on simulated manipulation tasks show that Screw Attention matches or outperforms other network architectures, achieving high success rates on LIBERO‑Spatial with far fewer parameters and maintaining performance under frame convention changes and pose noise.
Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable forensic cues. Recent cross-reconstruction based met...
The paper introduces Diffusion-Augmented Markov Decision Processes (DA‑MDPs), a framework that extends Maximum Entropy Reinforcement Learning to diffusion-based policies. DA‑MDPs treat each reverse‑diffusion step as an RL decision, deriving a tractable reverse‑KL bound that decomposes across denoising transitions and yields diffusion‑augmented soft rewards, value functions, and policy objectives. The authors implement this framework with PPO, REPPO, and a maximum‑entropy WPO variant, showing improved continuous‑control performance, higher success rates on manipulation tasks, and memory‑efficient training with action chunking.
arXiv:2605.18727v2 Announce Type: replace-cross Abstract: Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing sce...
FoCLIP is a framework that creates adversarial examples to manipulate CLIP-based image quality metrics by reducing the alignment between image and text features. It uses stochastic gradient descent to combine feature alignment, score distribution balancing, and pixel‑guard regularization, enabling high CLIPscore predictions while maintaining visual fidelity. Experiments on artistic prompts and ImageNet show significant CLIPscore gains, and the authors also propose a color‑channel sensitivity detection method that achieves 91% accuracy.
Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving proposes EMPlan, a hybrid trajectory planning method that combines sparse anchors with an offset refinement module for low-latency, high-accuracy predictions. The approach uses a two-stage training paradigm—pretraining followed by reward-guided fine-tuning—to improve safety without extra inference cost, leveraging rule-based reward signals and unpaired preference supervision. EMPlan is evaluated on the non-reactive NAVSIM benchmark, achieving a favorable balance between planning accuracy and efficiency under real-time constraints.
DiffWAM is a geometry‑conditioned navigation world‑action model that transforms predictive features from a frozen video foundation model into continuous camera trajectories, eliminating the need for future‑video synthesis and multi‑frame reconstruction during deployment. Its Grid‑Motion module preserves spatial‑temporal motion associations, while Latent2Pose grounds them with first‑frame geometry to recover metrically meaningful 3D motion. The system, complemented by FastDreamer for asynchronous trajectory handoff, achieves a trajectory RMSE of 0.3492 m and a 74.40 % endpoint success rate on the DiffWAM‑1000 benchmark, with real‑world tests showing complex UAV behaviors and an onboard implementation reaching 1.08 s latency on NVIDIA Jetson AGX Thor.
arXiv:2609.38864v1 Announce Type: new Abstract: Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as...
arXiv:2609.39623v1 Announce Type: new Abstract: The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images wh...
arXiv:2609.38285v1 Announce Type: cross Abstract: Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Add...
arXiv:2609.39145v1 Announce Type: cross Abstract: Unreliable visual inputs can harm task performance and cause potential physical safety risks for vision-language-action (VLA) models. We analyze how...
arXiv:2609.39304v1 Announce Type: cross Abstract: When an off-the-shelf coding agent is used directly as a robot policy, observing a browser-based 3D interface through screenshots and acting by posin...
arXiv:2609.39599v1 Announce Type: cross Abstract: 3D visuomotor policies provide a strong foundation for spatially precise manipulation, yet current text-to-3D policies struggle to follow unseen fine...
arXiv:2609.38620v1 Announce Type: new Abstract: Neural implicit representations have had a significant impact on scene reconstruction by enabling robots to build continuous, differentiable, and high-...