21 accepted papers
Articulation modeling enables robots to learn joint parameters of articulated objects for effective manipulation which can then be used downstream for skill learning or planning. Existing approaches often rely on prior knowledge about the objects, such as the number or type of joints. Some of these …
Classical manipulator motion planners work across different robot embodiments [1]. However they plan on a pre-specified static environment representation, and are not scalable to unseen dynamic environments. Neural Motion Planners (NMPs) [2] are an appealing alternative to conventional planners as t
This work consists of two main components: designing a hardware-software co-design, MT+, for adapting the Mikami-Tabuchi algorithm for on-board path planning by drones in a 3D environment; and development of a specialized custom hardware accelerator CDU, as a part of MT+, for parallel collision dete…
We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representati…
In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory with the supervision provided by language instructions. Learning longer horizon tasks is challenging with techniques such as reinforcement learning and behavior cloning. Previous approaches have split t…
Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observatio…
The field of Learning from Demonstration enables end-users, who are not robotics experts, to shape robot behavior. However, using human demonstrations to teach robots to solve long-horizon problems by leveraging the hierarchical structure of the task is still an unsolved problem. Prior work has yet…
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy. This procedure is comprised of two steps; (1) Policy Specification, i.e. humans specifying the behavior they would like their companion robot to accomp
Robots must be capable of learning from previously solved tasks and generalizing that knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot assistance in the real world. While multi-task learning research has produced agents capable of performing multiple tasks,
Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning setting which requires hierarchical abstractions. Prior work has st…
Modern Reinforcement Learning (RL) algorithms are not sample efficient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be repurposed to comple…
Enabling human operators to interact with robotic agents using natural language would allow non-experts to intuitively instruct these agents. Towards this goal, we propose a novel Transformer-based model which enables a user to guide a robot arm through a 3D multi-step manipulation task with natural…
We propose Action-Oriented Semantic Maps (AOSMs), a representation that enables a robot to acquire object manipulation behaviors and semantic information about the environment from a human teacher with a Mixed Reality Head-Mounted Display (MR-HMD). AOSMs are a representation that captures both: a) h…
Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object's type such as "scissors" and/or visual attributes such as "red," thus limiting the robot to only known ob…
Enabling robots to learn tasks and follow instructions as easily as humans is important for many real-world robot applications. Previous approaches have applied machine learning to teach the mapping from language to low dimensional symbolic representations constructed by hand, using demonstration tr…
With increasing autonomy, robots like drones are increasingly accessible to untrained users. Most users control drones using a low-level interface, such as a radio-controlled (RC) controller. For a wider adoption of these technologies by the public, a much higher-level interface, such as natural lan…
We develop a system to disambiguate object instances within the same class based on simple physical descriptions. The system takes as input a natural language phrase and a depth image containing a segmented object and predicts how similar the observed object is to the object described by the phrase.…
In order to intuitively and efficiently collaborate with humans, robots must learn to complete tasks specified using natural language. We represent natural language instructions as goal-state reward functions specified using lambda calculus. Using reward functions as language representations allows…
Often times, natural language commands issued to robots not only specify a particular target configuration or goal state but also outline constraints on how the robot goes about its execution. That is, the path taken to achieving some goal state is given equal importance to the goal state itself. On…
Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like 'grab a pallet' or a low-level action like 'tilt back a little bit.' While robots are also capa…