Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on dexterous hands, poses significant challenges for effective tactile feature learning, especially for 3D tactile data, as there are no large standardized datasets and no strong pretrained backbones. To address these challenges, we propose a novel canonical representation that reduces the difficulty of 3D tactile feature learning and further introduces a force-based selfsupervised pretraining task to capture both local and net force features, which are crucial for dexterous manipulation. Our method achieves an average success rate of 78% across four fine-grained, contact-rich dexterous manipulation tasks in realworld experiments, demonstrating effectiveness and robustness compared to other methods. Further analysis shows that our method fully utilizes both spatial and force information from 3D tactile data to accomplish the tasks. The videos can be viewed at https://3dtacdex.github.io.

Effectively utilizing multi-sensory data is important for robots to generalize across diverse tasks. However, the heterogeneous nature of these modalities makes fusion challenging. Existing methods propose strategies to obtain comprehensively fused features but often ignore the fact that each modality requires different levels of attention at different manipulation stages. To address this, we propose a force-guided attention fusion module that adaptively adjusts the weights of visual and tactile features without human labeling. We also introduce a self-supervised future force prediction auxiliary task to reinforce the tactile modality, improve data imbalance, and encourage proper adjustment. Our method achieves an average success rate of 93% across three fine-grained, contact-rich tasks in real-world experiments. Further analysis shows that our policy appropriately adjusts attention to each modality at different manipulation stages. The videos can be viewed at https://adaptac-dex.github.io/.

Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs. In the first stage, we predict a scene-decoupled, contact- and collision-aware representation—sparse IBS—as the optimization target. Sparse IBS compactly encodes the geometric and contact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining a high grasp success rate across diverse objects and complex scenes.

The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field (GraspGF), and a history-conditional residual policy. GraspGF learns 'how' to grasp by estimating the gradient of a synthesised success grasping example set, while the residual policy determines 'when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at https://sites.google.com/view/graspgf.

Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation even at the baby level are challenging to solve through reinforcement learning (RL). The difficulty lies in the high degrees of freedom and the required cooperation among heterogeneous agents (e.g., joints of fingers). In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects. Tasks in Bi-DexHands are first designed to match human-level motor skills according to literature in cognitive science, and then are built in Issac Gym; this enables highly efficient RL trainings, reaching 30,000+ FPS by only one single NVIDIA RTX 3090. We provide a comprehensive benchmark for popular RL algorithms under different settings; this includes multi-agent RL, offline RL, multi-task RL, and meta RL. Our results show that PPO type on-policy algorithms can learn to solve simple manipulation tasks that are equivalent up to 48-month human baby (e.g., catching a flying object, opening a bottle), while multi-agent RL can further help to learn manipulations that require skilled bimanual cooperation (e.g., lifting a pot, stacking blocks). Despite the success on each individual task, when it comes to mastering multiple manipulation skills, existing RL algorithms fail to work in most of the multi-task and the few-shot learning tasks, which calls for more future development from the RL community. Our project is open-sourced at https://github.com/PKU-MARL/DexterousHands.

Interior permanent magnet synchronous machines (IPMSM) often work under flux weakening state in electrical vehicles for higher speed. But unavoidable faults might occur during that process, after which the power transistors are switched off immediately and the battery packs are charged through the three-phase uncontrolled rectifier composed of six flywheel diodes. That is also renowned as uncontrolled generation (UCG) operation which needs to be well understood to avoid damage to either inverter or battery. In order to achieve more accurate analytical results, this paper proposes an improved steady-state model of the drive system for UCG analysis. On the basis of the model, the parameter dependencies are detailed. The voltage and current characteristics of UCG and the effectiveness of the proposed steady-state model are verified by experimental results.

This paper directly uses the machine windings to discharge the capacitor voltage in the PMSM drive systems when an emergency happens to electric vehicles. The winding-based method needs to control the d, q-axis current in the motor and it requires accurate position information during the whole discharge process. Whereas, the position sensor installed in the motor is likely to fail due to the violent vibration and impact in the emergency. Considering the position sensor faults, this paper proposes a more reliable current control algorithm based on a sliding mode position estimator to bleed the capacitor voltage. The proposed discharge strategy treats the machine as a generator so as to regulate the DC-bus voltage, and takes advantage of the maximum discharge capacity of the drive system and manages to maintain the discharge time within 2.7 s for the studied system. Simulation and experimental results are presented to verify the effectiveness of the proposed algorithm.