Contact feedback is essential for contact-rich robotic manipulation, as it allows the robot to detect subtle interaction changes and adjust its actions accordingly. Six-axis force-torque sensors are commonly used to obtain contact feedback, but their high cost and fragility have discouraged many researchers from adopting them in contact-rich tasks. To offer a more cost-efficient and easy-accessible source of contact feedback, we present ShapeForce, a low-cost, plug-and-play soft wrist that provides force-like signals for contact-rich robotic manipulation. Inspired by how humans rely on relative force changes in contact rather than precise force magnitudes, ShapeForce converts external force and torque into measurable deformations of its compliant core, which are then estimated via marker-based pose tracking and converted into force-like signals. Our design eliminates the need for calibration or specialized electronics to obtain exact values, and instead focuses on capturing force and torque changes sufficient for enabling contact-rich manipulation. Extensive experiments across diverse contact-rich tasks and manipulation policies demonstrate that ShapeForce delivers performance comparable to six-axis force-torque sensors at an extremely low cost.

Robotic manipulation tasks are contact-rich, yet most imitation learning (IL) approaches rely primarily on vision, which struggles to capture stiffness, roughness, slip, and other fine interaction cues. Tactile signals can address this gap, but existing sensors often require expensive, delicate, or integration-heavy hardware. In this work, we introduce MicCheck, a plug-and-play acoustic sensing approach that repurposes an off-the-shelf Bluetooth pin microphone as a low-cost contact sensor. The microphone clips into a 3D-printed gripper insert and streams audio via a standard USB receiver, requiring no custom electronics or drivers. Despite its simplicity, the microphone provides signals informative enough for both perception and control. In material classification, it achieves 92.9% accuracy on a 10-class benchmark across four interaction types (tap, knock, slow press, drag). For manipulation, integrating pin microphone into an IL pipeline with open source hardware improves the success rate on picking and pouring task from 0.40 to 0.80 and enables reliable execution of contact-rich skills such as unplugging and sound-based sorting. Compared with high-resolution tactile sensors, pin microphones trade spatial detail for cost and ease of integration, offering a practical pathway for deploying acoustic contact sensing in low-cost robot setups.

Learning from real-world robot demonstrations holds promise for interacting with complex real-world environments. However, the complexity and variability of interaction dynamics often cause purely positional controllers to struggle with contacts or varying payloads. To address this, we propose a Heterogeneous Meta-Control (HMC) framework for Loco-Manipulation that adaptively stitches multiple control modalities: position, impedance, and hybrid force-position. We first introduce an interface, HMC-Controller, for blending actions from different control profiles continuously in the torque space. HMC-Controller facilitates both teleoperation and policy deployment. Then, to learn a robust force-aware policy, we propose HMC-Policy to unify different controllers into a heterogeneous architecture. We adopt a mixture-of-experts style routing to learn from large-scale position-only data and fine-grained force-aware demonstrations. Experiments on a real humanoid robot show over 50% relative improvement vs. baselines on challenging tasks such as compliant table wiping and drawer opening, demonstrating the efficacy of HMC.

Glovity addresses key challenges in contact-rich tasks by providing intuitive wrench and tactile feedback, while overcoming embodiment gaps through precise retargeting. User studies demonstrate significant improvements: wrench feedback boosts success rates in book-flipping tasks from 48% to 78% and reduces completion time by 25%, while fingertip calibration enhances thin-object grasping success significantly compared to commercial glove. Furthermore, incorporating wrench signals into imitation learning (via DP-R3M) achieves high success rate in novel contact-rich scenarios, such as adaptive page flipping and force-aware handovers. All hardware designs, software will be open-sourced.

Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, robotic wrists are equally critical, often playing a greater role than the end effector itself. Many conventional wrist designs fall short in human environments because they are too large or rely on rigid, high-reduction actuators that cannot support dynamic, contact-rich tasks. Some designs address these issues using backdrivable quasi-direct drive (QDD) actuators and compact form factors. However, they are often difficult to model and control due to coupled kinematics or high mechanical inertia. We present DexWrist, a robotic wrist that is designed to advance robotic manipulation in highly constrained environments, enable dynamic and contact-rich tasks, and simplify policy learning. DexWrist provides low-impedance actuation, low inertia, integrated proprioception, high speed, and a large workspace. Together, these capabilities support robust learning-based manipulation. DexWrist accelerates policy learning by: (i) enabling faster teleoperation for scalable data collection, (ii) simplifying the learned function through shorter trajectories and decoupled degrees of freedom (DOFs), (iii) providing natural backdrivability for safe contact without complex compliant controllers, and (iv) expanding the manipulation workspace in cluttered scenes. In our experiments, DexWrist improved policy success rates by 50-55% and reduced task completion times by a factor of 3-5. More details about the wrist can be found at https://dexwrist.csail.mit.edu.

Vision-Language-Action (VLA) models have shown remarkable achievements, driven by the rich implicit knowledge of their vision-language components. However, achieving generalist robotic agents demands precise grounding into physical interactions, especially in contact-rich scenarios where fine-grained force control is essential. We advance VLAs' implicit knowledge beyond identifying what to do, towards guiding how to physically interact with real world. This paper introduces Tactile-VLA, a novel framework that deeply fuses vision, language, action, and tactile sensing. This framework incorporates a hybrid position-force controller to translate the model's intentions into precise physical actions and a reasoning module that allows the robot to adapt its strategy based on tactile feedback. Experiments demonstrate Tactile-VLA's effectiveness and generalizability in three key aspects: (1) enabling tactile-aware instruction following, (2) utilizing tactile-relevant commonsense, and (3) facilitating adaptive tactile-involved reasoning. A key finding is that the VLM's prior knowledge already contains semantic understanding of physical interaction; by connecting it to the robot's tactile sensors with only a few demonstrations, we can activate this prior knowledge to achieve zero-shot generalization in contact-rich tasks.

This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.

-- Ensuring safety in reinforcement learning (RL)- based robotic systems is a critical challenge, especially in contact-rich tasks within unstructured environments. While the state-of-the-art safe RL approaches mitigate risks through safe exploration or high-level recovery mechanisms, they often overlook low-level execution safety, where reflexive responses to potential hazards are crucial. Similarly, variable impedance control (VIC) enhances safety by adjusting the robot's mechanical response, yet lacks a systematic way to adapt parameters, such as stiffness and damping throughout the task. In this paper, we propose Bresa, a Bio-inspired Reflexive Hierarchical Safe RL method inspired by biological reflexes. Our method decouples task learning from safety learning, incorporating a safety critic network that evaluates action risks and operates at a higher frequency than the task solver . Unlike existing recovery-based methods, our safety critic functions at a low-level control layer, allowing real-time intervention when unsafe conditions arise. The task-solving RL policy, running at a lower frequency, focuses on high-level planning (decision-making), while the safety critic ensures instantaneous safety corrections. We validate Bresa on multiple tasks including a contact-rich robotic task, demonstrating its reflexive ability to enhance safety, and adaptability in unforeseen dynamic environments. Our results show that Bresa outperforms the baseline, providing a robust and reflexive safety mechanism that bridges the gap between high-level planning and low-level execution. I. INTRODUCTION Robotic actions in the real world present two major challenges: the complexity of unstructured environments and the safety hazards associated with physical interactions [1]. RL-based robotic systems have the potential to address both challenges to enable effective automated learning and exploration in such environments [2]. Traditionally, the complexity challenge has received significant attention, while the safety challenge has gained focus more recently, especially in contact-rich tasks [1].

Humans can accomplish complex contact-rich tasks using vision and touch, with highly reactive capabilities such as quick adjustments to environmental changes and adaptive control of contact forces; however, this remains challenging for robots. Existing visual imitation learning (IL) approaches rely on action chunking to model complex behaviors, which lacks the ability to respond instantly to real-time tactile feedback during the chunk execution. Furthermore, most teleoperation systems struggle to provide fine-grained tactile / force feedback, which limits the range of tasks that can be performed. To address these challenges, we introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality (AR), along with Reactive Diffusion Policy (RDP), a novel slow-fast visual-tactile imitation learning algorithm for learning contact-rich manipulation skills. RDP employs a two-level hierarchy: (1) a slow latent diffusion policy for predicting high-level action chunks in latent space at low frequency, (2) a fast asymmetric tokenizer for closed-loop tactile feedback control at high frequency. This design enables both complex trajectory modeling and quick reactive behavior within a unified framework. Through extensive evaluation across three challenging contact-rich tasks, RDP significantly improves performance compared to state-of-the-art visual IL baselines through rapid response to tactile / force feedback. Furthermore, experiments show that RDP is applicable across different tactile / force sensors. Code and videos are available on https://reactive-diffusion-policy.github.io/.

-- Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects. Policies without passivity guarantees can result in system instability, posing risks to robots, their environments, and human operators. In this work, we investigate the limitations of traditional RL policies when deployed in contact-rich tasks and explore the combination of energy-based passive control with safe RL in both training and deployment to answer these challenges. Firstly, we introduce energy-based constraints in our safe RL formulation to train passivity-aware RL agents. Secondly, we add a passivity filter on the agent output for passivity-ensured control during deployment. We conduct comparative studies on a contact-rich robotic maze exploration task, evaluating the effects of learning passivity-aware policies and the importance of passivity-ensured control. The experiments demonstrate that a passivity-agnostic RL policy easily violates energy constraints in deployment, even though it achieves high task completion in training. The results show that our proposed approach guarantees control stability through passivity filtering and improves the energy efficiency through passivity-aware training. A video of real-world experiments is available as supplementary material. I. INTRODUCTION In recent years, RL has earned increasing attention and success in addressing complex decision-making and control problems, especially in robotic applications [1]. From manipulation tasks to autonomous navigation, RL offers the potential to achieve unprecedented performance by learning optimal control policies.