Papers Robot Manipulation
“Robot Manipulation” 태그가 달린 논문 826편 · 필터 해제
DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are…
Robot ManipulationGTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes
Robotic manipulation tasks are often decomposed into behaviors or skills. However, one often needs to predefine these behaviors for specific tasks or try to cover a wide range of tasks using generic skills. As a result, …
Robot ManipulationCosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements
Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing methods are largely confined to simple, planar tasks and fail to handle c…
Robot ManipulationGRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation
Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks remains challenging. Task success often hinges on subtle, view-depe…
Robot ManipulationVisual GroundingRiemann-1.0: An Embodied World Action Model for Physical AI
We introduce Riemann-1.0, a fully causal autoregressive World Action Model for embodied intelligence. Riemann-1.0 jointly models multi-view visual observations, robot states, and embodiment-specific actions within a unif…
Robot ManipulationTemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation
Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not reliably capture recent physical change. This is especially problema…
Robot ManipulationPredVLA: A Sub-Million-Parameter Predictive-Coding Policy for Robot Manipulation
Large pretrained vision-language-action models dominate modern robot-manipulation benchmarks, but it remains unclear how much model scale is necessary for strong language-conditioned control, or whether fundamentally dif…
Robot ManipulationStreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models
Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a single-frame paradigm, limiting their ability to retain past observatio…
Robot ManipulationLM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction
Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: actions are exposed, but their explanatory state is not. They provide no native …
Robot ManipulationInstructMove: A Text-Indispensable Benchmark for Instruction-Following Manipulation
Vision-language-action (VLA) models have made general-purpose robot manipulation increasingly plausible by conditioning robot actions on natural-language instructions. A key test of such generality is whether policies ac…
Instruction FollowingRobot ManipulationSpatial ReasoningBeyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning
Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a policy that fails cannot learn from that failure without additional human d…
Reinforcement LearningRobot ManipulationDreamHand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery
Egocentric video offers scalable manipulation data for embodied AI, yet recovering metric 3D hand trajectories remains challenging due to severe object occlusion and frequent out-of-sight gaps. Existing single-frame and …
Robot ManipulationWhat Matters for Latent Actions in Robot Learning
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact surrogates for physical actions. Despite …
Robot ManipulationHiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation
World action models jointly predict future visual observations and actions, whereas existing tactile-aware variants typically represent future touch as an image or latent stream without modeling the physical dependencies…
Robot ManipulationDream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation
Learning robotic policies requires dense rewards that remain informative when behavior departs from successful demonstrations. Progress-based rewards estimate how far an observation has advanced along a nominal successfu…
Robot ManipulationCompCPZ: Preserving Multi-Modal Intent in Language-Guided Robot Manipulation
A robot asked to "place the cup near the red plate or the blue plate" may reach the centroid between them and appear geometrically successful, while satisfying neither disjunct of the instruction. This silent semantic fa…
Robot ManipulationORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performance under ideal conditions but often remain sensitive to small execution errors an…
Robot ManipulationDon't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory
Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the individual skills, yet the chain still fails: errors compound beyond …
Robot Manipulationτ_0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation
Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision …
Robot ManipulationUnified Condition-Action Modeling for Accurate One-Step Action Generation
Robot manipulation requires policies that are both accurate and efficient, as robot control must respond to changing observations under tight latency constraints. Recent diffusion and flow policies are promising, but the…
Representation LearningRobot Manipulation