{"total":12,"items":[{"citing_arxiv_id":"2606.25222","ref_index":18,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Swazure: Swarm Measurement of Pose for Flying Light Specks","primary_cat":"cs.RO","submitted_at":"2026-06-23T22:40:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Swazure enables FLS swarms to cooperatively measure relative poses for 3D point-cloud displays, achieving 100% neighbor positioning for medium FLS sizes and using Move Obstructing heuristic to resolve ~30% of obstructions in worst cases.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22145","ref_index":56,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System","primary_cat":"cs.RO","submitted_at":"2026-06-20T16:54:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Zero-shot sim-to-real transfer of independently trained RL policies for cart-pole swing-up and stabilization is achieved via sensitivity-guided domain randomization, linear curriculum learning, and first-order action smoothing with Simulink switching logic.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20645","ref_index":35,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion","primary_cat":"cs.RO","submitted_at":"2026-06-06T10:25:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06077","ref_index":11,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"3D Underwater Path Planning via Generative Flow Field Surrogates","primary_cat":"cs.RO","submitted_at":"2026-06-04T12:14:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"cGAN surrogates recover 45-60% of CFD energy savings and high-velocity wake avoidance in 3D AUV path planning while running at 28-146 microsecond inference speeds across 19,800 trajectories.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19009","ref_index":18,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Adversarial Stress Testing of SPARK Humanoid Safety Filters","primary_cat":"cs.RO","submitted_at":"2026-05-18T18:32:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Replicates SPARK humanoid safety filters and stress-tests them under crowding, noise, and delays, showing trade-offs in goal tracking versus collision reduction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13428","ref_index":12,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"SID: Sliding into Distribution for Robust Few-Demonstration Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-13T12:22:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SID achieves approximately 90% success on six real-world manipulation tasks with only two demonstrations under out-of-distribution initializations, with less than 10% performance drop under distractors and disturbances.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Kalm: Keypoint abstraction using large models for object-relative imitation learning. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pages 8307-8314. IEEE, 2025. [11] Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine. One-shot visual imitation learning via meta-learning, 2017. URL https://arxiv.org/abs/1709. 04905. [12] Jianfeng Gao, Zhi Tao, No 'emie Jaquier, and Tamim Asfour. K-vil: Keypoints-based visual imitation learn- ing.IEEE Transactions on Robotics, 39(5):3888- 3908, October 2023. ISSN 1941-0468. doi: 10.1109/ tro.2023.3286074. URL http://dx.doi.org/10.1109/TRO. 2023.3286074. [13] Haoran Geng, Ziming Li, Yiran Geng, Jiayi Chen, Hao Dong, and He Wang. Partmanip: Learning cross-category"},{"citing_arxiv_id":"2605.10696","ref_index":7,"ref_count":2,"confidence":0.5,"is_internal_anchor":false,"paper_title":"VRA: Grounding Discrete-Time Joint Acceleration in Voltage-Constrained Actuation","primary_cat":"cs.RO","submitted_at":"2026-05-11T15:11:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VRA grounds discrete-time joint acceleration commands in voltage-constrained actuator physics to eliminate unrealizable accelerations and reduce oscillations in electric motor systems.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"regions after high-level commands are generated [4, 18, 7]. Input saturation control methods focus on maintaining stability after actuator saturation has already occurred, treating satura- TABLE I: Relationship of Joint-Level Methods MethodPost-hoc Post-hoc Target V oltage Realizable Viable Zhang et al. [23]✓Feasible Set ✓ Wen et al. [20]✓Torque Command Kang et al. [7]✓Torque Command Morimoto et al. [13] - VRA (Ours)✓Acceleration Set✓ tion as an execution-side effect [20]. In both cases, feasibility is enforced at the command level and remains decoupled from kinematic constraints. In contrast, our approach is also post- hoc but differs fundamentally in where and how post-hoc handling is applied. VRA operates at the joint acceleration"},{"citing_arxiv_id":"2605.09545","ref_index":1,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Diagnostic Certificates of Data Quality and Regression Identifiability for Koopman Identification","primary_cat":"math.OC","submitted_at":"2026-05-10T14:01:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper introduces diagnostic certificates that separately assess state-space coverage, lifted-feature nondegeneracy, and regression-spectrum quality for Koopman and EDMDc identification, with theoretical guarantees on the smallest singular value under a population spectral gap.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09344","ref_index":26,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"PECMAN: Perception-enabled Collaborative Multi-Agent Navigation in Unknown Environments","primary_cat":"cs.RO","submitted_at":"2026-05-10T05:44:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"PECMAN lets multiple robots share perception and morph their individual RRT* trees to replan paths in unknown environments, reducing team completion time by up to 52 percent with near-100 percent success in 28,000 simulations and real-robot tests.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"by neighboring agents, thus wasting resources and delaying planning. To address the above problems, this paper extends the single-agent adaptive navigation strategy of SMART-3D to develop a novel algorithm called perception-enabled collabo- rative multi-agent navigation (PECMAN). B. Related Work Three lines of multi-agent research are relevant.Multi- Agent Path Finding (MAPF).MAPF [26], [27], [28] methods are scalable for coordinating hundreds of agents on known graphs with synchronized discrete timesteps. H ¨onig et al. [29] note that both these assumptions break down on physical robots in real environments. Conflict-based Search (CBS) [26] finds optimal solutions, but it suffers from computational explosion in narrow corridors [30]."},{"citing_arxiv_id":"2605.09216","ref_index":46,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Continuum Robot Modeling with Action Conditioned Flow Matching","primary_cat":"cs.RO","submitted_at":"2026-05-09T23:22:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A conditional point-cloud flow matching model maps motor actuation to 3D geometry of tendon-driven continuum robots and outperforms prior self-modeling methods on simulated and real 2- and 3-module hardware.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"routing and external loading.IEEE Transactions on Robotics, 27(6):1033-1044, 2011. doi: 10.1109/TRO. 2011.2160469. [45] Matteo Antonio Russo, S. M. Hadi Sadati, Xin Dong, Abdelkhalick Mohammad, Ian D. Walker, Christos Bergeles, Kai Xu, and Dragos A. Axinte. Continuum robots: An overview.Advanced Intelligent Systems, 5 (5):2200367, 2023. doi: 10.1002/aisy.202200367. [46] Sung-Chul Ryu and Pierre E. Dupont. FBG-based shape sensing tubes for continuum robots. InProceed- ings of the IEEE International Conference on Robotics and Automation (ICRA), pages 3531-3536, 2014. doi: 10.1109/ICRA.2014.6907368. [47] Chengnan Shentu and Jessica Burgner-Kahrs. Universal- jointed tendon-driven continuum robot: Design, kine- matic modeling, and locomotion in narrow tubes."},{"citing_arxiv_id":"2508.13677","ref_index":42,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Non-linear stochastic trajectory optimisation","primary_cat":"math.OC","submitted_at":"2025-08-19T09:27:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SODA uses differential algebra and adaptive Gaussian mixtures to solve chance-constrained nonlinear trajectory optimization problems for space missions with non-Gaussian uncertainties.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.10329","ref_index":7,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots","primary_cat":"cs.RO","submitted_at":"2024-02-15T21:11:50+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"UMI enables zero-shot deployment of robot manipulation policies trained solely on portable human demonstrations captured with custom handheld grippers, supporting dynamic bimanual tasks across novel environments and objects.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}