Robots that Collaborate: Sequential Asymmetric Imitation for Learning Coupled Robot Policies

Collaborative mobile manipulation requires robots to coordinate with a partially observed partner while physically interacting through shared objects. This is difficult because failures often arise not from poor local skills, but from mistimed waiting, yielding, pulling, releasing, or repositioning. We study this problem with two bimanual mobile manipulators coupled through rigid and deformable objects. We propose Sequential Asymmetric Imitation (SAI), a single-teleoperator curriculum for learning coupled multi-robot behaviors without synchronized dual-operator demonstrations or explicit inter-robot communication. SAI trains Robot A from unilateral demonstrations with a compliant human partner, trains Robot B against the deployed Robot A policy, and then refines Robot A using sparse interventions near coordination failures. This staged process exposes the policies to increasingly realistic partner behaviors, including delay, phase mismatch,insufficient yielding, and interaction conflict. Across real-world dual-robot manipulation tasks, SAI improves task success, phase synchronization, and partner-contingent yielding over independent imitation and curriculum-ablation baselines. These results suggest that physically coupled collaboration can be learned through the structure of the imitation curriculum, rather than through synchronized multi-operator demonstrations or explicit coordination mechanisms.Project page:http://cyc0429.github.io/sai-project-page/

PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation

Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open workspaces remains fragile under unexpected local scene dynamics, such as moving objects, transient occlusions, or disturbances near the intended motion. Existing runtime monitors often rely on global observation anomalies, policy uncertainty, or frame-level visual changes, and struggle to distinguish task-relevant execution risk from benign visual variation. We introduce PATCH, an action-chunk-conditioned latent patch innovation monitor for deployment-time intervention. Given the active action chunk, PATCH defines a projected execution corridor, predicts latent patch evolution inside it, and accumulates persistent residuals unexplained by the robot's own motion. These residuals form a localized intervention signal that allows PATCH-Router to pause execution, select an available recovery source, and resume the original policy once localized innovation subsides. Experiments on real robot rollout data show that PATCH produces more stable and context-relevant triggers than competing runtime monitors. Real-robot deployment further demonstrates monitor-driven intervention and policy resumption for disturbance-aware manipulation. Project Page: https://yananzhou5555.github.io/PATCH/.

PoseCompass: Intelligent Synthetic Pose Selection for Visual Localization

In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-tuning data quality and coverage. While recent methods leverage 3D Gaussian Splatting (3DGS) for novel view synthesis-based data augmentation, random sampling generates redundant views and noisy samples from poorly reconstructed regions. To mitigate this research gap, we propose PoseCompass, an intelligent pose selection pipeline for 3DGS-based APR. PoseCompass formulates synthetic pose selection and derives a value-based pose ranking mechanism to identify informative poses. The ranking integrates three dimensions: Localization Difficulty, favoring challenging regions; Coverage Novelty, exploring under-sampled areas; and Rendering Observability, filtering artifacts and noise. PoseCompass then generates trajectory-constrained candidates, selects the top-K ranked poses, and synthesizes views using 3DGS with lightweight diffusion-based alignment. Finally, the pose regressor is fine-tuned on mixed real and synthetic data. We evaluate PoseCompass on 7-Scenes, where it reduces adaptation time from 15.2 to 5.1 minutes, a 3x speedup, while cutting median pose errors by 53.8 percent and significantly outperforming random baselines.

TriPilot-FF: Coordinated Whole-Body Teleoperation with Force Feedback

Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem: operators must coordinate a wheeled base and two arms while reasoning about obstacles and contact. Existing interfaces are predominantly hand-centric (e.g., VR controllers and joysticks), leaving foot-operated channels underexplored for continuous base control. We present TriPilot-FF, an open-source whole-body teleoperation system for a custom bimanual mobile manipulator that introduces a foot-operated pedal with lidar-driven pedal haptics, coupled with upper-body bimanual leader-follower teleoperation. Using only a low-cost base-mounted lidar, TriPilot-FF renders a resistive pedal cue from proximity-to-obstacle signals in the commanded direction, shaping operator commands toward collision-averse behaviour without an explicit collision-avoidance controller. The system also supports arm-side force reflection for contact awareness and provides real-time force and visual guidance of bimanual manipulability to prompt mobile base repositioning, thereby improving reach. We demonstrate the capability of TriPilot-FF to effectively ``co-pilot'' the human operator over long time-horizons and tasks requiring precise mobile base movement and coordination. Finally, we incorporate teleoperation feedback signals into an Action Chunking with Transformers (ACT) policy and demonstrate improved performance when the additional information is available. We release the pedal device design, full software stack, and conduct extensive real-world evaluations on a bimanual wheeled platform. The project page of TriPilot-FF is http://bit.ly/46H3ZJT.

GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks

Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sensitive groups. While extensive studies have examined attacks targeting either objective, strategies that simultaneously degrade both utility and fairness remain largely unexplored. To bridge this gap, we introduce the Dual-Facet Attack (DFA), a novel threat model that concurrently undermines predictive accuracy and group fairness. Two variants, Synchronous DFA (S-DFA) and Split DFA (Sp-DFA), are further proposed to capture distinct real-world collusion scenarios. Experimental results show that existing robust FL defenses, including hybrid aggregation schemes, fail to resist DFAs effectively. To counter these threats, we propose GuardFed, a self-adaptive defense framework that maintains a fairness-aware reference model using a small amount of clean server data augmented with synthetic samples. In each training round, GuardFed computes a dual-perspective trust score for every client by jointly evaluating its utility deviation and fairness degradation, thereby enabling selective aggregation of trustworthy updates. Extensive experiments on real-world datasets demonstrate that GuardFed consistently preserves both accuracy and fairness under diverse non-IID and adversarial conditions, achieving state-of-the-art performance compared with existing robust FL methods.