Embodied communication: How robots and people communicate through physical interaction
Early research on physical human–robot interaction (pHRI) has necessarily focused on
device design—the creation of compliant and sensorized hardware, such as exoskeletons …
device design—the creation of compliant and sensorized hardware, such as exoskeletons …
Physically assistive robots: A systematic review of mobile and manipulator robots that physically assist people with disabilities
A Nanavati, V Ranganeni… - Annual Review of Control …, 2023 - annualreviews.org
More than 1 billion people in the world are estimated to experience significant disability.
These disabilities can impact people's ability to independently conduct activities of daily …
These disabilities can impact people's ability to independently conduct activities of daily …
Few-shot preference learning for human-in-the-loop rl
While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …
designing sufficiently informative reward functions for complex tasks has proven to be …
No, to the right: Online language corrections for robotic manipulation via shared autonomy
Systems for language-guided human-robot interaction must satisfy two key desiderata for
broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction …
broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction …
Inverse preference learning: Preference-based rl without a reward function
Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …
based Reinforcement Learning (RL) algorithms address these problems by learning reward …
Active preference-based gaussian process regression for reward learning
Designing reward functions is a challenging problem in AI and robotics. Humans usually
have a difficult time directly specifying all the desirable behaviors that a robot needs to …
have a difficult time directly specifying all the desirable behaviors that a robot needs to …
When humans aren't optimal: Robots that collaborate with risk-aware humans
In order to collaborate safely and efficiently, robots need to anticipate how their human
partners will behave. Some of today's robots model humans as if they were also robots, and …
partners will behave. Some of today's robots model humans as if they were also robots, and …
Active preference-based Gaussian process regression for reward learning and optimization
Designing reward functions is a difficult task in AI and robotics. The complex task of directly
specifying all the desirable behaviors a robot needs to optimize often proves challenging for …
specifying all the desirable behaviors a robot needs to optimize often proves challenging for …
Learning latent actions to control assistive robots
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own.
These arms are dexterous and high-dimensional; however, the interfaces people must use …
These arms are dexterous and high-dimensional; however, the interfaces people must use …
Laser: Learning a latent action space for efficient reinforcement learning
The process of learning a manipulation task depends strongly on the action space used for
exploration: posed in the incorrect action space, solving a task with reinforcement learning …
exploration: posed in the incorrect action space, solving a task with reinforcement learning …