develop contact-aware control

This study addresses a critical gap in the literature: the absence of a unified survey on robotic learning methods that integrate force and tactile perception, particularly regarding the synthesis of multimodal sensing and multi-stage system design. To bridge this gap, the paper introduces the TF-ART categorization framework—a novel, comprehensive architecture that systematically encompasses multimodal perceptual inputs, hierarchical action generation, and reactive low-level control. By integrating heterogeneous sensor encoding, multimodal perception fusion, and action refinement mechanisms, the framework elucidates the intrinsic relationships among existing approaches and clearly maps their design logic across the perception–decision–execution pipeline. This contribution provides a holistic perspective, theoretical foundation, and practical guidance for developing intelligent systems capable of rich physical interaction.

This work addresses the challenge of open-loop grasping under uncertainty in object shape and pose, where poor contact coordination often leads to slippage or failure. The authors propose a tactile feedback–based model predictive controller that enables coordinated multi-contact interaction and adaptive force modulation during both approach and grasp phases. Key innovations include perception-driven phase segmentation, arm–hand协同 compensation for pose errors, and a balanced adaptive force coordination mechanism. By analytically linking contact forces to joint motions, the method remains compatible with diverse grasp pose generation strategies. Evaluated across 15,000 simulations involving 478 objects and eight physical experiments, the approach significantly improves grasp success rates while effectively suppressing unintended object motion.

Addressing challenges in single- and multi-arm cooperative manipulation—including strong force-motion coupling, seamless switching between free motion and contact interaction over long time horizons, and lack of joint object-environment constraint modeling for inter-arm synchronization—this paper proposes a dynamically switchable three-modal control framework: pure planning, pure force control, and hybrid coordination. We introduce, for the first time, a task-driven dynamic modality allocation mechanism and systematically support joint object-environment constraint modeling in multi-arm settings. The method integrates impedance/admittance-based force control, nonlinear optimization-based motion planning (SQP/OC), real-time mode scheduling, and multibody dynamics modeling. Evaluated on long-horizon tasks—including single-arm assembly, dual-arm flipping, and tri-arm transport—the approach achieves a 42% reduction in contact force error, a 35% improvement in trajectory tracking accuracy, and a 98.7% task success rate.

This study addresses the significant challenge in hybrid force/motion control of relying exclusively on soft tactile sensing to online estimate contact forces and time-varying task frames. To overcome this, the work proposes an end-to-end mapping model based on single-frame optical tactile images, coupled with a self-annotated data acquisition pipeline, enabling real-time reconstruction of contact variables without requiring a nominal environment model. Furthermore, state estimation is achieved by integrating an extended Kalman filter (EKF) with robot proprioceptive data. The proposed approach is validated on a UR10 manipulator, demonstrating closed-loop contact force regulation under both linear and angular motions. These results confirm that high-precision hybrid control can be realized solely through tactile feedback, eliminating the need for external sensors.

This work addresses the challenge of force perception and distribution in multi-fingered humanoid robot hands when grasping objects with uneven mass distribution or unstable contacts. The authors propose a general control framework based on estimated contact forces, which leverages data from Xela magnetic tactile sensors to train a force estimation model. Rather than using raw tactile signals, the framework directly employs the estimated forces as input to coordinately regulate the motion of the torso, arms, wrists, and fingers, driving the center of pressure at the fingertips toward the centroid of the contact polygon to achieve stable grasps. The approach is compatible with any sensor capable of force estimation and enables dynamic force redistribution among fingers. Experimental results demonstrate an 82.7% success rate across five object-balancing tasks and 80% accuracy in multi-object scenarios.

This work addresses the prevalent instability of linear controllers—particularly LQR—in rich-contact manipulation under smooth contact dynamics. We systematically identify the root cause: strong coupling among contact transients, ill-conditioning of the Jacobian matrix, and state constraints. To overcome this, we propose a differential simulation framework grounded in smoothed contact modeling, thereby relaxing the implicit smoothness assumption inherent in gradient-based controllers. Furthermore, we introduce a co-optimization method that jointly designs robust open-loop trajectories and feedback gains. Empirical evaluation on over 300 high-contact-density trajectories executed on a dual-arm whole-body manipulation platform demonstrates that standard LQR achieves less than 40% closed-loop stability, whereas our approach significantly improves stability. The source code, models, and hardware experiment videos are publicly available.

This work addresses the common oversight in existing grasping methods of spatially non-uniform mechanical properties on object surfaces, which often leads to damage when contacts occur at fragile regions. The authors propose a novel grasping framework that integrates language instructions, 3D reconstruction, and physical awareness: leveraging SAM3D for language-guided 3D reconstruction, they perform physics-informed geometric analysis to generate local contact force tolerance maps, which are then used to filter and re-rank candidate grasp poses according to task consistency and force-map awareness. During execution, an adaptive impedance controller dynamically modulates finger stiffness based on contact points. This approach is the first to incorporate local mechanical tolerance maps throughout the entire grasping pipeline, reframing dexterous manipulation from a purely geometric problem into a joint optimization under physical constraints. Experiments demonstrate stable selection of high-strength contact regions and maintenance of grasp forces within safe thresholds on paper, plastic, and glass cups.

This study addresses the performance limitations of contact-rich manipulation caused by mismatches between preset stiffness or directions in traditional control and environmental constraints. We propose a proprioception-based reflex strategy trained in simulation with a frozen execution layer. By leveraging interaction primitives such as springs and planes alongside state-history mapping, the method translates task commands into joint targets, decoupling contact responses from command generation without requiring direct force or geometric measurements. Experimental results demonstrate that this approach maintains low contact forces during box lifting and outperforms baselines in surface following. Furthermore, it improves peg-in-hole insertion success rates by 36–58% while reducing contact forces by approximately 50%, thereby providing robust low-level execution capabilities for high-level planning.

This study addresses the challenge of maintaining physically stable grasps with dexterous robotic hands under dynamic contacts, modeling errors, and external disturbances. To this end, it proposes a real-time force regulation framework that operates without tactile sensing. By fusing geometric estimation with proprioceptive feedback, the method dynamically computes whole-hand contact force distributions that satisfy friction constraints and actuator consistency. Integrated with reactive motion planning, this approach enables closed-loop control across grasp acquisition, maintenance, and post-failure re-grasping for a 27-degree-of-freedom arm-hand system. Simulation results demonstrate significantly enhanced disturbance rejection capabilities, while hardware experiments validate stable grasping under complex contact evolution and rapid recovery following human-induced perturbations.

Existing approaches struggle to autonomously generate diverse sequences of contact locations and manipulation trajectories, limiting their ability to perform complex, contact-intensive tasks. This work proposes SCSP, a cascaded optimization framework that unifies contact location selection and trajectory planning within a single online-executable optimization pipeline. By integrating a surrogate contact model, mixed discrete-continuous optimization, and prior-guided real-time trajectory generation, SCSP effectively addresses the challenges posed by complementarity in contact dynamics and sparse gradients. Implemented on redundant robotic arms, the method enables joint online synthesis of contact points and motion trajectories. Extensive simulations and real-world experiments demonstrate its capability to produce diverse manipulation behaviors, exhibit robustness against dynamic modeling errors and perception noise, and generalize effectively across complex contact-rich tasks.

This work addresses the lack of human-like dexterity in tool-mediated manipulation caused by visual occlusion and underconstrained tactile perception. The authors propose a unified framework based on a parameterized equilibrium manifold (EM), integrating a differentiable contact model, tactile SLAM, and adaptive stiffness control. By establishing a physics-geometry duality, tactile state estimation is reformulated as manifold parameter inference. The study pioneers the use of tactile SLAM for joint discrete shape classification and continuous pose estimation in tool manipulation. Coupled with online trajectory replanning and uncertainty-aware impedance modulation, the system demonstrates robust performance in both simulation and over 260 real-world screw loosening trials, achieving success rates meeting standard operational requirements. Ablation studies confirm that tactile SLAM and adaptive stiffness significantly outperform fixed-impedance baselines, effectively preventing jamming during high-precision assembly tasks.