arXiv subjects

Salma Mozaffari

Publications and source records attributed to Salma Mozaffari.

3 recordsLinked to original sources

arXiv2026

Learning Diffusion Policies for Robotic Manipulation of Timber Joinery under Fabrication Uncertainty

Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated robotic assembly in construction, particularly for contact-rich manipulation tasks under minimal geometric clearance. This paper investigates the deployment of diffusion policy learning on construction-scale industrial robots to enable robust, high-precision assembly under such uncertainty, using tight-clearance mortise and tenon timber joinery as a representative case study. Sensory-motor diffusion policies are trained using teleoperated demonstrations collected from an industrial robotic workcell equipped with force/torque sensing. A two-phase experimental study evaluates baseline performance and robustness under randomized positional perturbations up to 10 mm, far exceeding the joint clearance. The best-performing policy achieved 100% success under nominal conditions and 75% average success under uncertainty. These results suggest that diffusion policies can improve robustness to fabrication-induced misalignment, representing a step toward reliable robotic assembly in construction under tight tolerances.

cs.RO↗

arXiv2026

A Latency-Aware Framework for Visuomotor Policy Learning on Industrial Robots

Industrial robots are increasingly deployed in construction and manufacturing tasks, where the deployment of end-to-end visuomotor policies is challenged by the observation-execution gap induced by observation, inference, and execution latencies. This gap is often significant on industrial robotic arms due to high-level control interfaces and slower closed-loop dynamics, making execution timing a dominant system-level concern. This paper presents a system-level, latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms. The framework integrates latency-calibrated multimodal sensing, data synchronization, a unified communication pipeline, and a teleoperation interface for collecting expert demonstrations. Within this framework, we formalize a latency-aware execution strategy that assigns timestamps to policy-predicted action sequences and schedules only temporally feasible actions according to their intended execution time, enabling asynchronous inference and execution without modifying policy architectures or training procedures. We evaluate the framework on a contact-rich assembly task while systematically varying inference latency and compare latency-aware execution against blocking and naive asynchronous baselines using identical policies and sensing modalities. Results show that latency-aware execution preserves smooth motion, compliant contact behavior, and task progression consistent with demonstrations across inference latencies of 100-500 ms. Latency-aware execution maintained task duration and motion smoothness within 13% and 9% of the demonstration reference, respectively, while avoiding the latency-dependent slowdown observed under blocking execution and the large contact-force overshoots produced by naive asynchronous execution.

cs.RO↗

arXiv2026

Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control

Construction robotics and automation offer promising means of improving productivity, alleviating workforce shortages, and reducing workers' exposure to physically demanding tasks. However, reliable contact-rich robotic assembly remains challenging under tight tolerances, fabrication inaccuracies, and uncertain contact dynamics. To address this challenge, we present a framework coupling diffusion policies trained on simulation-generated pose and force/torque data with an L1-inspired adaptive controller that corrects policy-predicted actions online to compensate for unmodeled contact dynamics. We benchmark the framework against baselines in timber joinery, pipe fitting, and sequential full-scale truss assembly. It achieves 100% success on single-task assemblies and 90-100% success across sequential truss assembly subtasks, with lower, more stable contact forces than the baselines. By enabling zero-shot sim-to-real transfer for force-aware contact-rich assembly, the framework reduces costly, labor-intensive real-world data collection for policy training and advances scalable, robust automation of multistage assembly, motivating extension to broader contact-rich manipulation tasks in construction.

cs.RO↗