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1. CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

   https://doi.org/10.1109/ICRA46639.2022.9811568  

   Wen, Bowen; Lian, Wenzhao; Bekris, Kostas E.; Schaal, Stefan (May 2022, IEEE International Conference on Robotics and Automation (ICRA))

   Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and annotate. There is also yet no consensus on proper representations for modeling or off-the-shelf tools for performing task-relevant grasps. This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation. To achieve this, the entire framework is trained solely in simulation, including supervised training with synthetic label generation and self-supervised, hand-object interaction. In the context of this framework, this paper proposes a novel, object-centric canonical representation at the category level, which allows establishing dense correspondence across object instances and transferring task-relevant grasps to novel instances. Extensive experiments on task-relevant grasping of densely-cluttered industrial objects are conducted in both simulation and real-world setups, demonstrating the effectiveness of the proposed framework. 

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2. Learning Generalizable Tool-use Skills through Trajectory Generation

   Qi, Carl; Wu, Yilin; Yu, Lifan; Liu, Haoyue; Jiang, Bowen; Lin, Xingyu; Held, David (September 2024, arxiv.org)

   Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior works based on affordance often make strong assumptions about the environments and cannot scale to more complex, contact-rich tasks. In this work, we tackle this challenge and explore how agents can learn to use previously unseen tools to manipulate deformable objects. We propose to learn a generative model of the tool-use trajectories as a sequence of tool point clouds, which generalizes to different tool shapes. Given any novel tool, we first generate a tool-use trajectory and then optimize the sequence of tool poses to align with the generated trajectory. We train a single model on four different challenging deformable object manipulation tasks, using demonstration data from only one tool per task. The model generalizes to various novel tools, significantly outperforming baselines. We further test our trained policy in the real world with unseen tools, where it achieves the performance comparable to human. 

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3. Generalizing Object-Centric Task-Axes Controllers using Keypoints

   Sharma, M.; Kroemer, O. (April 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. To achieve this it is often infeasible to train monolithic neural network policies across such large variations in object properties. Towards this generalization challenge, we propose to learn modular task policies which compose object-centric task-axes controllers. These task-axes controllers are parameterized by properties associated with underlying objects in the scene. We infer these controller parameters directly from visual input using multi- view dense correspondence learning. Our overall approach provides a simple and yet powerful framework for learning manipulation tasks. We empirically evaluate our approach on 3 different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry. 

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4. NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation

   https://doi.org/10.1126/scirobotics.adl0628  

   Suresh, Sudharshan; Qi, Haozhi; Wu, Tingfan; Fan, Taosha; Pineda, Luis; Lambeta, Mike; Malik, Jitendra; Kalakrishnan, Mrinal; Calandra, Roberto; Kaess, Michael; et al (November 2024, Science Robotics)

   Yashinski, Melisa (Ed.)

   To achieve human-level dexterity, robots must infer spatial awareness from multimodal sensing to reason over contact interactions. During in-hand manipulation of novel objects, such spatial awareness involves estimating the object’s pose and shape. The status quo for in-hand perception primarily uses vision and is restricted to tracking a priori known objects. Moreover, visual occlusion of objects in hand is imminent during manipulation, preventing current systems from pushing beyond tasks without occlusion. We combined vision and touch sensing on a multifingered hand to estimate an object’s pose and shape during in-hand manipulation. Our method, NeuralFeels, encodes object geometry by learning a neural field online and jointly tracks it by optimizing a pose graph problem. We studied multimodal in-hand perception in simulation and the real world, interacting with different objects via a proprioception-driven policy. Our experiments showed final reconstructionFscores of 81% and average pose drifts of 4.7 millimeters, which was further reduced to 2.3 millimeters with known object models. In addition, we observed that, under heavy visual occlusion, we could achieve improvements in tracking up to 94% compared with vision-only methods. Our results demonstrate that touch, at the very least, refines and, at the very best, disambiguates visual estimates during in-hand manipulation. We release our evaluation dataset of 70 experiments, FeelSight, as a step toward benchmarking in this domain. Our neural representation driven by multimodal sensing can serve as a perception backbone toward advancing robot dexterity. 

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5. Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies

   Marougkas, I; Ramesh, Dhruv M; Doerr, Joe H; Granados, E; Sivaramakrishnan, A; Boularias, A; Bekris, Kostas E (May 2025, IEEE International Conference on Robotics and Automation (ICRA))

   Object insertion under tight tolerances (less than 1mm) is an important but challenging assembly task as even small errors can result in undesirable contacts. Recent efforts focused on Reinforcement Learning (RL), which often depends on careful definition of dense reward functions. This work proposes an effective strategy for such tasks that integrates traditional model-based control with RL to achieve improved insertion accuracy. The policy is trained exclusively in simulation and is zero-shot transferred to the real system. It employs a potential field-based controller to acquire a model-based policy for inserting a plug into a socket given full observability in simulation. This policy is then integrated with residual RL, which is trained in simulation given only a sparse, goal-reaching reward. A curriculum scheme over observation noise and action magnitude is used for training the residual RL policy. Both policy components use as input the SE(3) poses of both the plug and the socket and return the plug's SE(3) pose transform, which is executed by a robotic arm using a controller. The integrated policy is deployed on the real system without further training or fine-tuning, given a visual SE(3) object tracker. The proposed solution and alternatives are evaluated across a variety of objects and conditions in simulation and reality. The proposed approach outperforms recent RL-based methods in this domain and prior efforts with hybrid policies. Ablations highlight the impact of each component of the approach. 

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