Recent development in the Wire arc additive manufacturing (WAAM) provides a promising
alternative for fabricating high value-added medium to large metal components for many …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …

For 3D object manipulation, methods that build an explicit 3D representation perform better
than those relying only on camera images. But using explicit 3D representations like voxels …

Imitation learning from human demonstrations has shown impressive performance in
robotics. However, most results focus on table-top manipulation, lacking the mobility and …

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot
enables a robot to rearrange objects in a scene, by first inferring a text description of those …

We propose a sim-to-real framework for dexterous manipulation which can generalize to
new objects of the same category in the real world. The key of our framework is to train the …

Task specification is at the core of programming autonomous robots. A low-effort modality for
task specification is critical for engagement of non-expert end users and ultimate adoption of …

Offline reinforcement learning (RL) enables learning control policies by utilizing only prior
experience, without any online interaction. This can allow robots to acquire generalizable …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …