Yasunori Toshimitsu

Nico Gürtler, Felix Widmaier, Cansu Sancaktar et al. · deepmind

Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not necessarily translate to real robots, in particular for tasks involving complex interactions with the environment. The Real Robot Challenge 2022 therefore served as a bridge between the RL and robotics communities by allowing participants to experiment remotely with a real robot - as easily as in simulation. In the last years, offline reinforcement learning has matured into a promising paradigm for learning from pre-collected datasets, alleviating the reliance on expensive online interactions. We therefore asked the participants to learn two dexterous manipulation tasks involving pushing, grasping, and in-hand orientation from provided real-robot datasets. An extensive software documentation and an initial stage based on a simulation of the real set-up made the competition particularly accessible. By giving each team plenty of access budget to evaluate their offline-learned policies on a cluster of seven identical real TriFinger platforms, we organized an exciting competition for machine learners and roboticists alike. In this work we state the rules of the competition, present the methods used by the winning teams and compare their results with a benchmark of state-of-the-art offline RL algorithms on the challenge datasets.

Misato Sonoda, Ronan Hinchet, Amirhossein Kazemipour et al. · eth-zurich, mit

Robotic manipulation in unstructured environments requires end-effectors that combine high kinematic dexterity with physical compliance. While traditional rigid hands rely on complex external sensors for safe interaction, electrohydraulic actuators offer a promising alternative. This paper presents the design, control, and evaluation of a novel musculoskeletal robotic hand architecture powered entirely by remote Peano-HASEL actuators, specifically optimized for safe manipulation. By relocating the actuators to the forearm, we functionally isolate the grasping interface from electrical hazards while maintaining a slim, human-like profile. To address the inherently limited linear contraction of these soft actuators, we integrate a 1:2 pulley routing mechanism that mechanically amplifies tendon displacement. The resulting system prioritizes compliant interaction over high payload capacity, leveraging the intrinsic force-limiting characteristics of the actuators to provide a high level of inherent safety. Furthermore, this physical safety is augmented by the self-sensing nature of the HASEL actuators. By simply monitoring the operating current, we achieve real-time grasp detection and closed-loop contact-aware control without relying on external force transducers or encoders. Experimental results validate the system's dexterity and inherent safety, demonstrating the successful execution of various grasp taxonomies and the non-destructive grasping of highly fragile objects, such as a paper balloon. These findings highlight a significant step toward simplified, inherently compliant soft robotic manipulation.

Davide Liconti, Yuning Zhou, Yasunori Toshimitsu et al.

Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands. In practice, the term is often used inconsistently, with different systems evaluated under disparate criteria, making meaningful comparisons across designs difficult. This highlights the need for a unified, performance-based definition of dexterity grounded in measurable outcomes rather than proxy metrics. In this work, we introduce POMDAR, a comprehensive dexterity benchmark that formalizes dexterity as task performance across a structured set of manipulation and grasping motions. The benchmark was systematically derived from established taxonomies in human motor control. It is implemented in both real-world and simulation and includes four manipulation configurations: vertical and horizontal configurations, continuous rotation, and pure grasping. The task designs contain mechanical scaffolding to constrain task motion, suppress compensatory strategies, and enable metrics to be measured unambiguously. We define a quantitative scoring metric combining task correctness and execution speed, effectively measuring dexterity as throughput. This enables objective, reproducible, and interpretable evaluation across different hand designs. POMDAR provides an open-source, standardized, and taxonomy-grounded benchmark for consistent comparison and evaluation of anthropomorphic robot hands to facilitate a systematic advancement of dexterous manipulation platforms. CAD, simulation files, and evaluation videos are publicly available at https://srl-ethz.github.io/POMDAR/.

Oliver Fischer, Yasunori Toshimitsu, Amirhossein Kazemipour et al.

Dynamic motions are a key feature of robotic arms, enabling them to perform tasks quickly and efficiently. Soft continuum manipulators do not currently consider dynamic parameters when operating in task space. This shortcoming makes existing soft robots slow and limits their ability to deal with external forces, especially during object manipulation. We address this issue by using dynamic operational space control. Our control approach takes into account the dynamic parameters of the 3D continuum arm and introduces new models that enable multi-segment soft manipulators to operate smoothly in task space. Advanced control methods, previously afforded only to rigid robots, are now adapted to soft robots; for example, potential field avoidance was previously only shown for rigid robots and is now extended to soft robots. Using our approach, a soft manipulator can now achieve a variety of tasks that were previously not possible: we evaluate the manipulator's performance in closed-loop controlled experiments such as pick-and-place, obstacle avoidance, throwing objects using an attached soft gripper, and deliberately applying forces to a surface by drawing with a grasped piece of chalk. Besides the newly enabled skills, our approach improves tracking accuracy by 59% and increases speed by a factor of 19.3 compared to state of the art for task space control. With these newfound abilities, soft robots can start to challenge rigid robots in the field of manipulation. Our inherently safe and compliant soft robot moves the future of robotic manipulation towards a cageless setup where humans and robots work in parallel.

Amirhossein Kazemipour, Oliver Fischer, Yasunori Toshimitsu et al.

Soft robots are made of compliant materials and perform tasks that are challenging for rigid robots. However, their continuum nature makes it difficult to develop model-based control strategies. This work presents a robust model-based control scheme for soft continuum robots. Our dynamic model is based on the Euler-Lagrange approach, but it uses a more accurate description of the robot's inertia and does not include oversimplified assumptions. Based on this model, we introduce an adaptive sliding mode control scheme, which is robust against model parameter uncertainties and unknown input disturbances. We perform a series of experiments with a physical soft continuum arm to evaluate the effectiveness of our controller at tracking task-space trajectory under different payloads. The tracking performance of the controller is around 38\% more accurate than that of a state-of-the-art controller, i.e., the inverse dynamics method. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of segments. With this control strategy, soft robotic object manipulation can become more accurate while remaining robust to disturbances.

Yasunori Toshimitsu, Ki Wan Wong, Thomas Buchner et al.

Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and considered the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator's mass and the material properties, and predicts in real-time the soft robot's behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model's and sensor's accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.