Visual servo based on traditional image matching methods often requires accurate keypoint correspondence for high precision control. However, keypoint detection or matching tends to fail in challenging scenarios with inconsistent illuminations or textureless objects, resulting significant performance degradation. Previous approaches, including our proposed Correspondence encoded Neural image Servo policy (CNS), attempted to alleviate these issues by integrating neural control strategies. While CNS shows certain improvement against error correspondence over conventional image-based controllers, it could not fully resolve the limitations arising from poor keypoint detection and matching. In this paper, we continue to address this problem and propose a new solution: Probabilistic Correspondence Encoded Neural Image Servo (CNSv2). CNSv2 leverages probabilistic feature matching to improve robustness in challenging scenarios. By redesigning the architecture to condition on multimodal feature matching, CNSv2 achieves high precision, improved robustness across diverse scenes and runs in real-time. We validate CNSv2 with simulations and real-world experiments, demonstrating its effectiveness in overcoming the limitations of detector-based methods in visual servo tasks.

Deploying visual servo controller to novel scenes with uncertain parameters requires additional manual effort for calibration. Traditional methods tackle this problem by online estimating the Jacobian matrix. However, they struggle in challenging scenes due to intrinsic limitations. For instance, image-based uncalibrated visual servo requires tracking a fixed set of points, which is impractical in texture-less scenes. Position-based uncalibrated visual servo necessitates absolute scale of translation, which requires depth sensor or model-based pose estimator, introducing extra hardware cost or model complexity. Recent advances in neural network-based visual servoing have shown improvement in convergence, precision and generalization compared to traditional methods. However, the uncalibrated neural visual servo remains underexplored. In this paper, we propose a structured Jacobian estimator for neural-based visual servo controller, enabling zero-shot transfer to novel environments with unknown extrinsic and scene scale. Stability of pose error is analyzed under the bounded calibration error assumption. Moreover, we propose an automatic control gain scheduler to accelerate the convergence while maintaining high success rate and precision. The scheduling behavior is analyzed through greedy optimal control. Our method is validated with simulated and real-world experiments.

Task-oriented dexterous grasp generation aims to generate stable and functional grasps that enable a robotic hand to effectively interact with objects to accomplish specific tasks. However, generating high-dimensional hand configurations that seamlessly adapt to diverse task requirements and object geometries remains a significant challenge. In this paper, we propose a novel pipeline called ColaDex to address this challenging problem. The core idea of ColaDex is to leverage a vision-language models (VLMs) to select the dexterous grasp from a set of candidates that aligns well with the task description. To this end, we first introduce a contact-guided optimization method to generate a set of high-quality grasp candidates around the object through analytical optimization. Subsequently, to effectively prompt VLMs with the sampled numerous grasp candidates, we propose an object-centric approach that adaptively represents a group of candidates as prototypical contact maps, learned based on the geometric relationships between the grasping hand and object shape. We then feed the task requirement and the generated prototypical contact maps into the VLM, enabling it to reason about grasp-object interactions and assess their alignment with the given task, ultimately selecting the grasp that best aligns with the task requirement. Extensive experiments demonstrate that our prototypical contact map is a more informative prompting mechanism than conventional RGB images, enabling ColaDex to consistently generate high-quality task-oriented grasps and achieve a high success rate across diverse objects and tasks.

Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the robustness of imitation learning policies for these tasks. Data-driven solutions face the challenge of high real-world data costs, while simulation data, despite its low costs, is limited by the sim-to-real gap. We identify the root cause of gripper state ambiguity as the lack of tactile feedback. To address this, we propose a novel approach employing pseudo-tactile as feedback, inspired by the idea of using a force-controlled gripper as a tactile sensor. This method enhances policy robustness without additional data collection and hardware involvement, while providing a noise-free binary gripper state observation for the policy and thus facilitating pure simulation learning to unleash the power of simulation. Experimental results across three real-world grasp-based tasks demonstrate the necessity, effectiveness, and efficiency of our approach. Videos are available on Project Page.

We focus on the task of unknown object rearrangement, where a robot is supposed to reconfigure the objects into a desired goal configuration specified by an RGB-D image. Recent works explore unknown object rearrangement systems by incorporating learning-based perception modules. However, they are sensitive to perception error, and pay less attention to task-level performance. In this article, we aim to develop an effective system for unknown object rearrangement amidst perception noise. We theoretically reveal that the noisy perception impacts grasp and place in a decoupled way, and show such a decoupled structure is valuable to improve task optimality. We propose grasp, see, and place (GSP), a dual-loop system with the decoupled structure as prior. For the inner loop, we learn a see policy for self-confident in-hand object matching. For the outer loop, we learn a grasp policy aware of object matching and grasp capability guided by task-level rewards. We leverage the foundation model CLIP for object matching, policy learning, and self-termination. A series of experiments indicate that GSP can conduct unknown object rearrangement with higher completion rates and fewer steps.

Visual servoing (VS) is a widely used technique in industries where there are hundreds of robots, but it requires accurate camera calibration including camera intrinsic and extrinsic parameters. However, it is labour-intensive to calibrate robots one-by-one in practical use. In this paper, we propose a neural uncalibrated VS policy (NUVS) that can adapt to calibration disturbances with an adaption mechanism and a control-oriented guidance. It bridges the disturbance adaption of classical VS methods and the large convergence of learning-based VS methods. NUVS estimates the calibration embedding from past observations and servos to the desired pose under the supervision of a PBVS that can access the ground truth in simulation. With this adaption mechanism, NUVS outperforms the classical IBUVS algorithm when facing large initial camera pose offsets under the calibration disturbance. Supplementary material in: https://sites.google.com/view/neural-uncalibrated-vs

We focus on the task of language-conditioned grasping in clutter, in which a robot is supposed to grasp the target object based on a language instruction. Previous works separately conduct visual grounding to localize the target object, and generate a grasp for that object. However, these works require object labels or visual attributes for grounding, which calls for handcrafted rules in planner and restricts the range of language instructions. In this paper, we propose to jointly model vision, language and action with object-centric representation. Our method is applicable under more flexible language instructions, and not limited by visual grounding error. Besides, by utilizing the powerful priors from the pre-trained multi-modal model and grasp model, sample efficiency is effectively improved and the sim2real problem is relived without additional data for transfer. A series of experiments carried out in simulation and real world indicate that our method can achieve better task success rate by less times of motion under more flexible language instructions. Moreover, our method is capable of generalizing better to scenarios with unseen objects and language instructions.

In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human's behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, which proves to be general to the unseen peg geometries. By putting the estimators into the closed-loop control with reinforcement learning, we further achieve higher or comparable success rate, efficiency, and robustness compared with the baseline methods. The policy is trained totally in simulation without any manual intervention. To achieve sim-to-real, a learnable segmentation module with automatic data collecting and labeling can be easily trained to decouple the perception and the policy, which helps the model trained in simulation quickly adapting to the real world with negligible effort. Results are presented in simulation and on a physical robot. Code, videos, and supplemental material are available at https://github.com/xieliang555/SFN.git

Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the network inevitably learns the representation of the background of the training data before figuring out the defection. They underperform in the inference stage once the context changed and can only be solved by training in every new settings. This eventually leads to the limitation in practical robotic applications where contexts keep varying. To cope with this, instead of training a network context by context and hoping it to generalize, why not stop misleading it with any limited context and start training it with pure simulation? In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all. A small defection detection layer utilizing the pose sensitivity of phase correlation between images is introduced and is followed by an outlier masking layer. The network is trained on randomly generated simulated data with simple shapes and is generalized across the real world. Finally, SSDS is validated on real-world collected data and demonstrates the ability that even when trained in cheap simulation, SSDS can still find small defections in the real world showing the effectiveness and its potential for practical applications. Code is available here

Assembly orders of components have direct influence on feasibility and efficiency of assembly process in manufacturing and are usually defined by experienced operators. To automate the assembly sequence generation process, we present a method using the idea of case-based reasoning, which can take advantage of experience of a reference assembly to generate the assembly sequence of a new assembly. First, a novel assembly representation method named assembly graph is present in which nodes indicating components’ 3D shape information and edges indicating the geometry constraints. Second, a similar components retrieve process is conducted based on assembly graph representation. Then, the assembly sequence is generated by applying the assembly order of retrieved components to the new ones. Next, the generated sequence is revised to satisfy the inherent constraints in the new assembly which is formulated as a contact graph. Finally, the revised sequence is stored into a case library with corresponding assembly model. We apply the proposed method to generate assembly sequences for chair assemblies and experimental results show its effectiveness and flexibility.