Papers Robot Manipulation
“Robot Manipulation” 태그가 달린 논문 839편 · 필터 해제
Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation
Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Real, an agentic policy framework that lea…
Robot ManipulationFlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting
Human hand-object demonstrations offer a reusable source of dexterous robot manipulation data, but transferring them across embodiments requires physically feasible retargeting. Existing physics-based approaches face lim…
Robot Manipulation3DROID: A Renderable 3D Gaussian Dataset with Measured Per-Scene Reliability
Robot manipulation models primarily reason from 2D observations while acting in the 3D physical world. To bridge this gap, recent work has augmented robot data with geometric priors such as depth, point clouds, and 3D tr…
Robot ManipulationPoint CloudsRoboCoach: World Models as Active Coaches for Compositional Robot Skills
Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstrations is costly. A practical self-improving system must decide both what t…
Robot ManipulationMotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefit…
Zero-shot GeneralizationRobot ManipulationVisual GroundingRecursive Harness Distillation across Agents for Robot Manipulation
A central goal in robotics is to enable manipulation across changing tasks and environments. Vision-language-action (VLA) models provide broad manipulation capabilities but can struggle when execution requires diagnosing…
Robot ManipulationInternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
World Action Models (WAMs) jointly model visual dynamics and action generation for generalist robot manipulation. A central challenge is to integrate priors from large-scale pretrained models---including visual dynamics,…
Robot ManipulationWorld Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal
General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them indirectly, to predict constraints or write programs, or give them a v…
Robot ManipulationSpatial ReasoningDeformSmith: Physics Harness-Guided Hierarchical Generation of Deformable Assets for Robot Manipulation
Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties. This is especially challenging for deformable objects, since text and images provide limi…
Robot ManipulationTEMPO: Learning Temporal Context for Dynamic Robot Manipulation
Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks because they operate on a single observation at inference time. We identif…
Robot ManipulationThe Latent That Never Was: A Forensic Re-run of the CVAE Ablation in Action Chunking Transformer
Action Chunking Transformers (ACT) are widely used to learn robot manipulation from demonstrations. Their conditional variational autoencoder includes an encoder meant to capture differences between demonstrations during…
Robot ManipulationWholeBodyWAM: Generalizing Pre-trained World-Action Priors to Humanoid Loco-Manipulation via WBC-Grounded Coordination
World Action Models (WAMs) offer a promising approach to general-purpose robot manipulation by jointly modeling visual dynamics and actions. However, most WAM studies focus on tabletop or arm-centric manipulation, while …
Robot ManipulationSAVLA: Symmetry-Aware Vision-Language-Action Models for Robotic Manipulation
Vision-language-action (VLA) models have become the dominant paradigm for language-conditioned robot manipulation. However, although images and language instructions inherently encode geometric information, VLAs acquire …
Robot ManipulationDUET-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 Manipulation