Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse robot models and enriched trajectory data using Jacobian-derived features. The dataset consists of 8,192 robots with varied inertial and frictional properties. Leveraging attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining Transformers with automated dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems

Deep learning methods have significantly enhanced motion planning for robotic manipulators by leveraging prior experiences within planning datasets. However, state-of-the-art neural motion planners are primarily trained on small datasets collected in manually generated workspaces, limiting their generalizability to out-of-distribution scenarios. Additionally, these planners often rely on monolithic network architectures that struggle to encode critical planning information. To address these challenges, we introduce Motion Policy with Dataset Synthesis powered by large language models (LLMs) and Fusion Action-Chunking Transformers (PerFACT), which incorporates two key components. Firstly, a novel LLM-powered workspace generation method, MotionGeneralizer, enables large-scale planning data collection by producing a diverse set of semantically feasible workspaces. Secondly, we introduce Fusion Motion Policy Networks (MpiNetsFusion), a generalist neural motion planner that uses a fusion action-chunking transformer to better encode planning signals and attend to multiple feature modalities. Leveraging MotionGeneralizer, we collect 3.5M trajectories to train and evaluate MpiNetsFusion against state-of-the-art planners, which shows that the proposed MpiNetsFusion can plan several times faster on the evaluated tasks.

Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; however, they typically require predefined functional assumptions, which poses significant challenges when dealing with unknown functional structures. To address this issue, this paper proposes a physics-inspired machine learning approach based on the Kolmogorov-Arnold Network (KAN) for static friction modeling of robotic joints. The method integrates spline activation functions with a symbolic regression mechanism, enabling model simplification and physical expression extraction through pruning and attribute scoring, while maintaining both high prediction accuracy and interpretability. We first validate the method's capability to accurately identify key parameters under known functional models, and further demonstrate its robustness and generalization ability under conditions with unknown functional structures and noisy data. Experiments conducted on both synthetic data and real friction data collected from a six-degree-of-freedom industrial manipulator show that the proposed method achieves a coefficient of determination greater than 0.95 across various tasks and successfully extracts concise and physically meaningful friction expressions. This study provides a new perspective for interpretable and data-driven robotic friction modeling with promising engineering applicability. Introduction In robotic operating systems, friction plays a crucial role in determining motion control accuracy, particularly in high-precision, low-velocity, and force-controlled tasks, where its influence becomes markedly pronounced.

In-Space Servicing, Assembly, and Manufacturing (ISAM) is a set of emerging operations that provides several benefits to improve the longevity, capacity, mobility, and expandability of existing and future space assets. Serial robotic manipulators are particularly vital in accomplishing ISAM operations, however, the complex perturbation forces and motions associated with movement of a robotic arm on a free-flying satellite presents a complex controls problem requiring additional study. While many dynamical models are developed, experimentally testing and validating these models is challenging given that the models operate in space, where satellites have six-degrees-of-freedom (6-DOF). This paper attempts to resolve those challenges by presenting the design and development of a new hardware-in-the-loop (HIL) experimental testbed utilized to emulate ISAM. This emulation will be accomplished by means of a 6-DOF UR3e robotic arm attached to a satellite bus. This satellite bus is mounted to a 1-DOF guide-rail system, enabling the satellite bus and robotic arm to move freely in one linear direction. This experimental ISAM emulation system will explore and validate models for space motion, serial robot manipulation, and contact mechanics. This is the author's original manuscript (pre-print) of the paper AAS 25-426, presented at the 35th AAS/AIAA Space Flight Mechanics Meeting, Kaua'i, Hawaii, January 19-23, 2025.INTRODUCTION The emerging capabilities offered by In-Space Servicing, Assembly, and Manufacturing (ISAM) can vastly expand the ranges of operation for in-space assets to improve reusability, mobility, ex-pandability, sustainability, and mission lifespans. ISAM operations permit servicing of existing satellites, repurposing and recycling of satellites, manufacturing and construction in-orbit, refueling, and upgrades to existing satellites.

Intelligent robotic disassembly of end-of-life (EOL) products has been a long-standing challenge in robotics. While machine learning techniques have shown promise, the lack of specialized hardware limits their application in real-world scenarios. We introduce DeGrip, a customized gripper designed for the disassembly of EOL computer desktops. DeGrip provides three degrees of freedom (DOF), enabling arbitrary configurations within the disassembly environment when mounted on a robotic manipulator. It employs a cable-driven transmission mechanism that reduces its overall size and enables operation in confined spaces. The wrist is designed to decouple the actuation of wrist and jaw joints. We also developed an EOL desktop disassembly environment in Isaac Sim to evaluate the effectiveness of DeGrip. The tasks were designed to demonstrate its ability to operate in confined spaces and disassemble components in arbitrary configurations. The evaluation results confirm the capability of DeGrip for EOL desktop disassembly.

Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a desired end-effector pose. Although analytical solvers are fast and efficient, they are limited to systems with low degrees-of-freedom and specific topological structures. Numerical optimization-based approaches are more general, but suffer from high computational costs and frequent convergence to spurious local minima. Recent efforts have explored the use of GPUs to combine sampling and optimization to enhance both the accuracy and speed of IK solvers. We build on this recent literature and introduce HJCD-IK, a GPU-accelerated, sampling-based hybrid solver that combines an orientation-aware greedy coordinate descent initialization scheme with a Jacobian-based polishing routine. This design enables our solver to improve both convergence speed and overall accuracy as compared to the state-of-the-art, consistently finding solutions along the accuracy-latency Pareto frontier and often achieving order-of-magnitude gains. In addition, our method produces a broad distribution of high-quality samples, yielding the lowest maximum mean discrepancy. We release our code open-source for the benefit of the community.

Accurate pose and velocity estimation is essential for effective spatial task planning in robotic manipulators. While centralized sensor fusion has traditionally been used to improve pose estimation accuracy, this paper presents a novel decentralized fusion approach to estimate both pose and velocity. We use dual-view measurements from an eye-in-hand and an eye-to-hand vision sensor configuration mounted on a manipulator to track a target object whose motion is modeled as random walk (stochastic acceleration model). The robot runs two independent adaptive extended Kalman filters formulated on a matrix Lie group, developed as part of this work. These filters predict poses and velocities on the manifold $\mathbb{SE}(3) \times \mathbb{R}^3 \times \mathbb{R}^3$ and update the state on the manifold $\mathbb{SE}(3)$. The final fused state comprising the fused pose and velocities of the target is obtained using a correlation-aware fusion rule on Lie groups. The proposed method is evaluated on a UFactory xArm 850 equipped with Intel RealSense cameras, tracking a moving target. Experimental results validate the effectiveness and robustness of the proposed decentralized dual-view estimation framework, showing consistent improvements over state-of-the-art methods.

--This paper presents a new task-space Non-singular Terminal Super-Twisting Sliding Mode (NT -STSM) controller with adaptive gains for robust trajectory tracking of a 7-DOF robotic manipulator. The proposed approach addresses the challenges of chattering, unknown disturbances, and rotational motion tracking, making it suited for high-DOF manipulators in dexterous manipulation tasks. A rigorous boundedness proof is provided, offering gain selection guidelines for practical implementation. Simulations and hardware experiments with external disturbances demonstrate the proposed controller's robust, accurate tracking with reduced control effort under unknown disturbances compared to other NT -STSM and conventional controllers. The results demonstrated that the proposed NT -STSM controller mitigates chattering and instability in complex motions, making it a viable solution for dexterous robotic manipulations and various industrial applications. HE development of robust control algorithms is necessary for industrial robotic manipulators in applications such as remote surgery, cooperative multi-robot manipulation, and handling varying payloads. These applications require precise trajectory tracking, robustness to disturbances, and energy-efficient control strategies. High degree-of-freedom (DOF) manipulators offer an extensive range of motion, however, their complex nonlinear dynamics, with model uncertainties and external disturbances, pose significant control challenges.

Neural Information Processing Systems http://nips.cc/

Ingenuity Labs Research Institute Queen's University Kingston, Canada matthew.pan@queensu.ca Abstract -- This study explores how human perceptions of a non-anthropomorphic robotic manipulator can be shaped by two key dimensions of behaviour: arousal, defined as the robot's movement energy and expressiveness, and attention, defined as the robot's capacity to selectively orient toward and engage with a user . We present an integrated behaviour system that applies and extends existing movement-centric design principles to non-anthropomorphic robots. Our system combines a gaze-like attention engine with an arousal-modulated motion layer to explore how expressive and interactive behaviours influence social perception in robotic manipulators. In a user study, we find that robots exhibiting high attention--actively directing their focus toward users--are perceived as warmer and more competent, intentional, and lifelike. In contrast, high arousal--characterized by fast, expansive, and energetic motions--increases perceptions of discomfort and disturbance. Importantly, a combination of focused attention and moderate arousal yields the highest ratings of trust and sociability, while excessive arousal diminishes social engagement.