There has been an emerging paradigm shift from the era of “internet AI” to “embodied AI,”
where AI algorithms and agents no longer learn from datasets of images, videos or text …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training
and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

The unprecedented rise in research interest in artificial intelligence (AI) and related areas,
such as computer vision, machine learning, robotics, and cognitive science, during the last …

Recent research in embodied AI has been boosted by the use of simulation environments to
develop and train robot learning approaches. However, the use of simulation has skewed …

Language-guided Embodied AI benchmarks requiring an agent to navigate an environment
and manipulate objects typically allow one-way communication: the human user gives a …

Embodied agents operating in human spaces must be able to master how their environment
works: what objects can the agent use, and how can it use them? We introduce a …

We present RCareWorld, a human-centric simulation world for physical and social robotic
caregiving designed with inputs from stakeholders. RCareWorld has realistic human models …

Progress in continual reinforcement learning has been limited due to several barriers to
entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this …