arXiv AI
Jun 8
Planning-aligned Token Compression for Long-Context Autonomous Driving
arXiv:2606. 07464v1 Announce Type: cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving.
Motion Planning in Compressed Representation Spaces
arXiv:2606. 30940v1 Announce Type: cross Abstract: Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in capturing the highly complex behavior required for solving tasks such as manipulation or navigation for autonomous vehicles.
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arXiv AI
Jun 9
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.
arXiv AI
Jun 19
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
arXiv AI
Jul 17
ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
arXiv:2607. 14424v1 Announce Type: cross Abstract: In recent years Flow Matching has become a prominent method for generative modeling robot motion generation.
arXiv AI
Jul 17
RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
arXiv:2607. 14187v1 Announce Type: new Abstract: Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved.
arXiv AI
Jul 1
MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents
arXiv:2606. 31167v1 Announce Type: cross Abstract: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.
arXiv Machine Learning
Jun 2
Semantic-Geometric Task Representations for Bimanual Manipulation from Human Demonstrations to Robot Action Planning
arXiv:2601. 11460v2 Announce Type: replace-cross Abstract: Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object involvement, and interaction geometry vary significantly across executions.
arXiv AI
Jun 24
FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning
arXiv:2606. 24231v1 Announce Type: new Abstract: Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor-based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground-truth trajectory.
Hugging Face Trending Papers
Jul 15
RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination.
arXiv AI
1d ago
Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
arXiv:2604. 07084v2 Announce Type: replace-cross Abstract: Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators.
arXiv AI
Jun 19
Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving
arXiv:2606. 20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories.
arXiv AI
Jul 14
Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification
arXiv:2607. 10706v1 Announce Type: cross Abstract: The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions.