This paper proposes Tac2Motion, a contact-aware reinforcement learning framework to facilitate the learning of contact-rich in-hand manipulation tasks, such as removing a lid. To this end, we propose tactile sensing-based reward shaping and incorporate the sensing into the observation space through embedding. The designed rewards encourage an agent to ensure firm grasping and smooth finger gaiting at the same time, leading to higher data efficiency and robust performance compared to the baseline. We verify the proposed framework on the opening a lid scenario, showing generalization of the trained policy into a couple of object types and various dynamics such as torsional friction. Lastly, the learned policy is demonstrated on the multi-fingered robot, Shadow Robot, showing that the control policy can be transferred to the real world. The video is available: https://youtu.be/poeJBPR7urQ.

Electric prosthetic hands should be lightweight to decrease the burden on the user, shaped like human hands for cosmetic purposes, and have motors inside to protect them from damage and dirt. In addition to the ability to perform daily activities, these features are essential for everyday use of the hand. In-hand manipulation is necessary to perform daily activities such as transitioning between different postures, particularly through rotational movements, such as reorienting cards before slot insertion and operating tools such as screwdrivers. However, currently used electric prosthetic hands only achieve static grasp postures, and existing manipulation approaches require either many motors, which makes the prosthesis heavy for daily use in the hand, or complex mechanisms that demand a large internal space and force external motor placement, complicating attachment and exposing the components to damage. Alternatively, we combine a single-axis thumb and optimized thumb positioning to achieve basic posture and in-hand manipulation, that is, the reorientation between precision and lateral grasps, using only four motors in a lightweight (311 g) prosthetic hand. Experimental validation using primitive objects of various widths (5-30 mm) and shapes (cylinders and prisms) resulted in success rates of 90-100% for reorientation tasks. The hand performed seal stamping and USB device insertion, as well as rotation to operate a screwdriver.

Reinforcement learning (RL) and sim-to-real transfer have advanced robotic manipulation of rigid objects. Yet, policies remain brittle when applied to articulated mechanisms due to contact-rich dynamics and under-modeled joint phenomena such as friction, stiction, backlash, and clearances. We address this challenge through dexterous in-hand manipulation of articulated tools using a robotic hand with reduced articulation and kinematic redundancy relative to the human hand. Our controller augments a simulation-trained base policy with a sensor-driven refinement learned from hardware demonstrations, conditioning on proprioception and target articulation states while fusing whole-hand tactile and force feedback with the policy's internal action intent via cross-attention-based integration. This design enables online adaptation to instance-specific articulation properties, stabilizes contact interactions, regulates internal forces, and coordinates coupled-link motion under perturbations. We validate our approach across a diversity of real-world examples, including scissors, pliers, minimally invasive surgical tools, and staplers. We achieve robust transfer from simulation to hardware, improved disturbance resilience, and generalization to previously unseen articulated tools, thereby reducing reliance on precise physical modeling in contact-rich settings.

Abstract--Despite progress in both traditional dexterous grasping pipelines and recent Vision-Language-Action (VLA) approaches, the grasp execution stage remains prone to pose inaccuracies, especially in long-horizon tasks, which undermines overall performance. T o address this "last-mile" challenge, we propose T acRefineNet, a tactile-only framework that achieves fine in-hand pose refinement of known objects in arbitrary target poses using multi-finger fingertip sensing. Our method iteratively adjusts the end-effector pose based on tactile feedback, aligning the object to the desired configuration. We design a multi-branch policy network that fuses tactile inputs from multiple fingers along with proprioception to predict precise control updates. T o train this policy, we combine large-scale simulated data from a physics-based tactile model in MuJoCo with real-world data collected from a physical system. Comparative experiments show that pretraining on simulated data and fine-tuning with a small amount of real data significantly improves performance over simulation-only training. T o our knowledge, this is the first method to enable arbitrary in-hand pose refinement via multi-finger tactile sensing alone. Project website is available at https://sites.google.com/view/tacrefinenet

Abstract-- Dexterous in-hand manipulation remains a foun-dational challenge in robotics, with progress often constrained by the prevailing paradigm of imitating the human hand. This anthropomorphic approach creates two critical barriers: 1) it limits robotic capabilities to tasks humans can already perform, and 2) it makes data collection for learning-based methods exceedingly difficult. Both challenges are caused by traditional force-closure which requires coordinating complex, multi-point contacts based on friction, normal force, and gravity to grasp an object. This makes teleoperated demonstrations unstable and amplifies the sim-to-real gap for reinforcement learning. In this work, we propose a paradigm shift: moving away from replicating human mechanics toward the design of novel robotic embodiments. We introduce the Suction Leap-Hand (SLeap Hand), a multi-fingered hand featuring integrated fingertip suction cups that realize a new form of suction-enabled dexterity. More importantly, this suction-based embodiment unlocks a new class of dexterous skills that are difficult or even impossible for the human hand, such as one-handed paper cutting and in-hand writing. Our work demonstrates that by moving beyond anthropomorphic constraints, novel embodiments can not only lower the barrier for collecting robust manipulation data but also enable the stable, single-handed completion of tasks that would typically require two human hands. Dexterous manipulation, the ability to reconfigure objects within a single hand, remains a grand challenge in robotics [1], [2]. The dominant paradigm for achieving this goal has been data-driven learning on anthropomorphic hands, an approach that has led to successes in grasping and reorientation [3], [4], [5].

In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently shown great success. However, the derived controllers are not yet useful in real-world scenarios because they often require a human operator to place the objects in suitable initial (grasping) states. Finding stable grasps that also promote the desired in-hand manipulation goal is an open problem. In this work, we propose a method for bridging this gap by leveraging the critic network of a reinforcement learning agent trained for in-hand manipulation to score and select initial grasps. Our experiments show that this method significantly increases the success rate of in-hand manipulation without requiring additional training. We also present an implementation of a full grasp manipulation pipeline on a real-world system, enabling autonomous grasping and reorientation even of unwieldy objects.

Figure 1: We propose Flow Before Imitation (FBI), a novel dynamic visuotactile imitation learning algorithm for dexterous in-hand manipulation. FBI's design enables two operational modes: with or without physical tactile sensors in the real world, largely extending the application scenarios. Abstract -- Dexterous in-hand manipulation is a long-standing challenge in robotics due to complex contact dynamics and partial observability. This paper introduces Flow Before Imitation (FBI), a visuotactile imitation learning framework that dynamically fuses tactile interactions with visual observations through motion dynamics. Unlike prior static fusion methods, FBI establishes a causal link between tactile signals and object motion via a dynamics-aware latent model. FBI employs a transformer-based interaction module to fuse flow-derived tactile features with visual inputs, training a one-step diffusion policy for real-time execution. Extensive experiments demonstrate that the proposed method outperforms the baseline methods in both simulation and the real world on two customized in-hand manipulation tasks and three standard dexterous manipulation tasks.

Human manipulation skills represent a pinnacle of their voluntary motor functions, requiring the coordination of many degrees of freedom and processing of high-dimensional sensor input to achieve such a high level of dexterity. Thus, we set out to answer whether the human hand, with its associated biomechanical properties, sensors, and control mechanisms, is an ideal that we should strive for in robotics-do we really need anthropomorphic robotic hands? This survey can help practitioners to make the trade-off between hand complexity and potential manipulation skills. We provide an overview of the human hand, a comparison of commercially available robotic and prosthetic hands, and a systematic review of hand mechanisms and skills that they are capable of. This leads to follow-up questions. What is the minimum requirement for mechanisms and sensors to implement most skills that a robot needs? What is missing to reach human-level dexterity? Can we improve upon human dexterity? Although complex five-fingered hands are often used as the ultimate goal for robotic manipulators, they are not necessary for all tasks. We found that wrist flexibility and finger abduction/adduction are important for manipulation capabilities. On the contrary, increasing the number of fingers, actuators, or degrees of freedom is often not necessary. Three fingers are a good compromise between simplicity and dexterity. Non-anthropomorphic hand designs with two opposing pairs of fingers or human hands with six fingers can further increase dexterity, suggesting that the human hand may not be the optimum.

This paper presents a method for online estimation of contact properties during in-hand sliding manipulation with a parallel gripper. We estimate the static and Coulomb friction as well as the contact radius from tactile measurements of contact forces and sliding velocities. The method is validated in both simulation and real-world experiments. Furthermore, we propose a heuristic to deal with fast slip-stick dynamics which can adversely affect the estimation.

The strength of the human hand lies in its ability to manipulate small objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle small objects effectively. This is why many automation tasks remain unsolved by robots. This paper presents an optimization-based framework for in-hand manipulation with a robotic hand equipped with compact Magnetic Tactile Sensors (MTSs). The small form factor of the robotic hand from Shadow Robot introduces challenges in estimating the state of the object while satisfying contact constraints. To address this, we formulate a trajectory optimization problem using Nonlinear Programming (NLP) for finger movements while ensuring contact points to change along the geometry of the fingers. Using the optimized trajectory from the solver, we implement and test an open-loop controller for rolling motion. To further enhance robustness and accuracy, we introduce a force controller for the fingers and a state estimator for the object utilizing MTSs. The proposed framework is validated through comparative experiments, showing that incorporating the force control with compliance consideration improves the accuracy and robustness of the rolling motion. Rolling an object with the force controller is 30\% more likely to succeed than running an open-loop controller. The demonstration video is available at https://youtu.be/6J_muL_AyE8.