6 accepted papers
In this study, we focus on enhancing the policy of a musculoskeletal arm to develop grasping abilities for objects of varying weights. The agent is modeled using MyoSuite, a platform with realistic biomechanics where muscles drive skeletal movement. We observed that optimizing only the control polic
A deformable agent can continuously adjust its morphology during training, allowing it to discover more suitable structures and outperform fixed-morphology counterparts in terrain-specific tasks. This adaptability is achieved through a joint optimization process consisting of two stages: the Skeleto
In real-world scenarios, many robotic manipulation tasks are hindered by occlusions and limited fields of view, posing significant challenges for passive observation-based models that rely on fixed or wrist-mounted cameras. In this letter, we investigate the problem of robotic manipulation under lim
3D generation from natural language offers significant potential to reduce expert manual modeling efforts and enhance accessibility to 3D assets. However, existing methods often yield unstructured meshes and exhibit poor interactivity, making them impractical for artistic workflows. To address these…
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their…
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to…