{"total":13,"items":[{"citing_arxiv_id":"2606.06491","ref_index":56,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies","primary_cat":"cs.RO","submitted_at":"2026-06-04T17:59:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TempoVLA learns a single VLA policy with controllable execution speed via variable-speed trajectory augmentation and explicit speed conditioning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02027","ref_index":8,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"World-Task Factorization for Robot Learning","primary_cat":"cs.RO","submitted_at":"2026-06-01T10:16:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces world-task factorization for robot policies using Bayesian evidence and AICON graph plus learned modulator, outperforming baselines with zero-shot generalization in heterogeneous robotics settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00499","ref_index":86,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"OptiWorld: Optimal Control for Video World Generation under Physical Constraints","primary_cat":"cs.CV","submitted_at":"2026-05-30T03:13:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"OptiWorld inserts a classical optimal-control layer that extracts a world state, plans an optimal trajectory on a geometric manifold under physical constraints, and renders the video conditioned on that trajectory.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00110","ref_index":33,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling","primary_cat":"cs.CV","submitted_at":"2026-05-27T03:38:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27314","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Riding the Shifting Potential: When Reactive Control Suffices for Multi-Goal Behavior","primary_cat":"cs.RO","submitted_at":"2026-05-26T17:24:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Nullspace projections on a graph-based model enable reactive control to achieve 100% success in non-convex planar pushing across 100 configurations, versus 0% for steepest descent and ~55% for diffusion policy, without training or retraining.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21811","ref_index":34,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Safe and Steerable Geometric Motion Policies for Robotic Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-20T23:16:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SafePBDS uses pullback control barrier functions and a task manifold action interface to generate certifiably safe, steerable motions on high-DOF robots from objectives defined on arbitrary geometric spaces.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.28197","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"OmniRobotHome: A Multi-Camera Platform for Real-Time Multiadic Human-Robot Interaction","primary_cat":"cs.RO","submitted_at":"2026-04-30T17:59:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A 48-camera residential platform delivers real-time occlusion-robust 3D perception and coordinated actuation for multi-human multi-robot interaction in a shared home workspace.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Collaboration in shared workspaces requires intention understanding, real-time adaptation, and proximity-aware safety. Handover methods condition on pre- dicted contact [48], receiver motion [24], and gaze [35]. Cooperative planners model human motion [6], learn interaction primitives [8], predict intention from skeleton cues [43], and learn strategies via imitation [46]. For safety, reactive [2, 38] and learning-based [10,55] controllers handle collision avoidance in dynamic settings. Existing work overwhelmingly studies dyadic interaction with onboard or single-viewpoint sensing; multiadic collaboration with task-coupled dependen- cies remains largely unexplored. 2.4 Human Motion Prediction and Behavior Memory Proactive assistance requires anticipating human actions."},{"citing_arxiv_id":"2604.07084","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models","primary_cat":"cs.RO","submitted_at":"2026-04-08T13:38:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.05493","ref_index":63,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots","primary_cat":"cs.RO","submitted_at":"2026-03-05T18:58:04+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"cuRoboV2 unifies B-spline optimization, GPU-native dense signed distance fields, and scalable whole-body kinematics and dynamics to achieve 99.7% success on payloaded manipulators and 99.6% collision-free IK on 48-DoF humanoids.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"column and then walking it to assign the nearest site to every voxel yields an 𝑂¹𝑁º-work, 𝑂¹1º-error distance transform per axis. Phase 3 reuses the identical sweep and coloring logic on axis-transposed data, so only three unique kernels are needed. The entire propagation produces the exact Euclidean Voronoi diagram, unlike approxi- mate iterative methods such as JFA [63], which requiredlog2 𝑁e¸ 3 passes and can miss sites in adversarial configurations. Because the number of kernel launches is fixed (five) and independent of scene content, the pipeline is fully compatible with CUDA graph capture. After propagation, the Euclidean distance at each voxel is computed from the stored site coordinates. Stage 3: Sign Recovery"},{"citing_arxiv_id":"2602.00992","ref_index":45,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Geometry-Aware Sampling-Based Motion Planning on Riemannian Manifolds","primary_cat":"cs.RO","submitted_at":"2026-02-01T03:14:46+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A sampling-based planner approximates Riemannian geodesic distances via midpoints with third-order accuracy and uses retractions plus natural gradients for local planning, producing lower-cost trajectories than Euclidean baselines on robotic arms and SE(2) systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.07813","ref_index":34,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies","primary_cat":"cs.RO","submitted_at":"2025-05-12T17:59:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DexWild co-trains dexterous robot policies on in-the-wild human hand interactions recorded with a low-cost system and limited robot data, achieving 68.5% success in unseen environments and 5.8x better cross-embodiment generalization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.01652","ref_index":60,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation","primary_cat":"cs.RO","submitted_at":"2024-09-03T06:45:22+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Oleynikova, A. Handa, F. Ramos, et al. Curobo: Parallelized collision-free robot motion generation. In 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages 8112-8119. IEEE, 2023. [59] T. Marcucci, J. Umenberger, P. Parrilo, and R. Tedrake. Shortest paths in graphs of convex sets. SIAM Journal on Optimization, 34(1):507-532, 2024. [60] N. D. Ratliff, J. Issac, D. Kappler, S. Birchfield, and D. Fox. Riemannian motion policies. arXiv preprint arXiv:1801.02854, 2018. [61] M. Posa, S. Kuindersma, and R. Tedrake. Optimization and stabilization of trajectories for constrained dynamical systems. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 1366-1373. IEEE, 2016."},{"citing_arxiv_id":"2307.05973","ref_index":125,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models","primary_cat":"cs.RO","submitted_at":"2023-07-12T07:40:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"VoxPoser uses LLMs to compose 3D value maps via VLM interaction for model-based synthesis of robust robot trajectories on open-set language-specified manipulation tasks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, pages 424-432. Springer, 2016. [124] L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE transactions on Robotics and Automation, 12(4):566-580, 1996. [125] N. D. Ratliff, J. Issac, D. Kappler, S. Birchfield, and D. Fox. Riemannian motion policies. arXiv preprint arXiv:1801.02854, 2018. [126] T. Marcucci, M. Petersen, D. von Wrangel, and R. Tedrake. Motion planning around obstacles with convex optimization. arXiv preprint arXiv:2205.04422, 2022. [127] J. Li, D. Li, S. Savarese, and S. Hoi. Blip-2: Bootstrapping language-image pre-training with"}],"limit":50,"offset":0}