8 accepted papers
In order to provide a robot with the ability to understand and react to a user's natural language inputs, the natural language must be connected to the robot's underlying representations of the world. Recently, large language models (LLMs) and 3D scene graphs (3DSGs) have become a popular choice for…
For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, e…
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. LLMs have been successfully used to help find and improve prompt candidates for single-step tasks. However, realistic tasks for agents are multi-step and introduce new challenges: (1) Prompt content is…
A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks. The planning performance of LLMs is significantly improved via prompting techniques, such as in-context learning or re-prompting with state feedb…
As robots operate alongside humans in shared spaces, such as homes and offices, it is essential to have an effective mechanism for interacting with them. Natural language offers an intuitive interface for communicating with robots, but most of the recent approaches to grounded language understanding…
Robust motion planners for unmanned ground vehicles must minimize risk while obeying vehicle mobility constraints. Algorithms such as the State Lattice (SL) utilize offline computation to generate expressive control sets which form recombinant search spaces, enabling the use of heuristic search to e…
Current methods of grounding natural language instructions do not include reactive or temporal components, making these methods unsuitable for instructions describing tasks as sets of conditional instructions. We introduce the Verifiable Distributed Correspondence Graph (V-DCG) model, which enables…
Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. Howev…