Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of object images and grasp labels. However, empirical grasping datasets are typically sparsely labeled (i.e., a small number of successful grasp labels**Labels refer to marking the image to indicate a successful robotic grasp. in each image). The data sparsity issue can lead to insufficient supervision and false-negative labels, and thus results in poor learning results. This paper proposes a Maximum Likelihood Grasp Sampling Loss (MLGSL) to tackle the data sparsity issue. The proposed method supposes that successful grasps are stochastically sampled from the predicted grasp distribution and maximizes the observing likelihood. MLGSL is utilized for training a fully convolutional network that generates thousands of grasps simultaneously. Training results suggest that models based on MLGSL can learn to grasp with datasets composing of 2 labels per image. Compared to previous works, which require training datasets of 16 labels per image, MLGSL is 8× more data-efficient. Meanwhile, physical robot experiments demonstrate an equivalent performance at a 90.7% grasp success rate on household objects. Codes and videos are available at [1].

Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is challenging due to the complex object shape, incomplete object information, and sensor noise. In this paper, we present a 6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a single-view depth image. First, an image encoder is used to extract the feature map from the input depth image, after which 3-DoF grasp regions are proposed from the feature map with a rotated region proposal network. Feature vectors that within the proposed grasp regions are then extracted and refined to 6-DoF grasps. The proposed model is trained offline with synthetic grasp data. To improve the robustness in reality and bridge the simulation-to-real gap, we further introduce a contrastive learning module and variant image processing techniques during the training. CGPN can locate collision-free grasps of an object using a single-view depth image within 0.5 second. Experiments on a physical robot further demonstrate the effectiveness of the algorithm. The experimental videos are available at [1].

Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment. Experimental videos are available at [1].

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.

Grasp planning for multi-fingered hands is computationally expensive due to the joint-contact coupling, surface nonlinearities and high dimensionality, thus is generally not affordable for real-time implementations. Traditional planning methods by optimization, sampling or learning work well in planning for parallel grippers but remain challenging for multi-fingered hands. This paper proposes a strategy called finger splitting, to plan precision grasps for multi-fingered hands starting from optimal parallel grasps. The finger splitting is optimized by a dual-stage iterative optimization including a contact point optimization (CPO) and a palm pose optimization (PPO), to gradually split fingers and adjust both the contact points and the palm pose. The dual-stage optimization is able to consider both the object grasp quality and hand manipulability, address the nonlinearities and coupling, and achieve efficient convergence within one second. Simulation results demonstrate the effectiveness of the proposed approach. The simulation video is available at [1].

Grasp planning is essential for robots to execute dexterous tasks. Solving the optimal grasps for various objects online, however, is challenging due to the heavy computation load during exhaustive sampling, and the difficulties to consider task requirements. This paper proposes a framework to combine analytic approach with learning for efficient grasp generation. The example grasps are taught by human demonstration and mapped to similar objects by a non-rigid transformation. The mapped grasps are evaluated analytically and refined by an orientation search to improve the grasp robustness and robot reachability. The proposed approach is able to plan high-quality grasps, avoid collision, satisfy task requirements, and achieve efficient online planning. The effectiveness of the proposed method is verified by a series of experiments.

Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform large-scale manipulation tasks for various objects, a multi-fingered robotic hand sometimes has to sequentially adjust its grasping gestures, i.e. the finger gaits, to address the workspace limits and guarantee the object stability. However, realizing finger gaits planning in dexterous manipulation is challenging due to the complicated grasp quality metrics, uncertainties on object shapes and dynamics (mass and moment of inertia), and unexpected slippage under uncertain contact dynamics. In this paper, a dual-stage optimization based planner is proposed to handle these challenges. In the first stage, a velocity-level finger gaits planner is introduced by combining object grasp quality with hand manipulability. The proposed finger gaits planner is computationally efficient and realizes finger gaiting without 3D model of the object. In the second stage, a robust manipulation controller using robust control and force optimization is proposed to address object dynamics uncertainties and external disturbances. The dual-stage planner is able to guarantee stability under unexpected slippage caused by uncertain contact dynamics. Moreover, it does not require velocity measurement or expensive 3D/6D tactile sensors. The proposed dual-stage optimization based planner is verified by simulations on Mujoco. The simulation video is available at [1].

To enhance the robotic manipulation of deformable objects, a robust state estimator is proposed to track the object configuration in real time. A Gaussian mixture model (GMM) is constructed to register the object nodes towards the noisy point cloud. To deal with occlusion, the coherent point drift (CPD) regularization is applied on the mixture model, so as to maintain the topological structure from previous sequences of data and to infer the object states in occluded area. The state estimation is further refined by running a dynamic simulation in parallel, which guarantees the estimates to satisfy the object's physical constraints. A series of rope tracking experiments are performed to evaluate the proposed state estimator. It is shown that the object can be tracked robustly with sensor noise, outliers and massive occlusion.

In the application of physical human-robot interaction (pHRI), the collaboration between human and robot can significantly improve the production efficiency through combination of the human's flexible intelligence and the robot's consistent performance. In this application, however, it is an important concern to ensure the safety of the human and the robot. In the human guidance programming scenario, the operator plans a collision-free path for the robot end-effector, but the robot body might collide with an obstacle while being guided by the operator. In this paper, a novel on-line velocity based collision avoidance algorithm is developed to solve the problem in this particular scenario. The proposed algorithm gives an explicit solution to deal with both collision avoidance and human guidance command at the same time, which provides the operator a better and safer lead through programming experience. The real-time experiment is performed on FANUC LR Mate 200 iD/7L in three different obstacle scenarios.