This video demonstrates task-based dialogues with an autonomous golf cart (work performed in conjunction with Indiana University Bloomington). Here, the robot uses pragmatic inference to determine that the human user has a false belief about the robot's current goals and asks for clarification.

This video demonstrates the system's ability to acquire new knowledge about objects, object parts, object functions, actions, and normative action restrictions from single instructions (work performed in collaboration with the Tufts Human-Robot Interaction Laboratory). This is different from statistics-based approaches that require the collection of a large amount of training data for the robot to learn something new. No training data is needed for our system!

This video shows a unique capability of our architecture to share knowledge among multiple robots quickly and efficiently: if you teach one robot something new, all other robots know it as well even if they have different physical embodiments (work performed in collaboration with the Tufts Human-Robot Interaction Laboratory).

This video shows our ability to take a fully teleoperated robot that was never meant for autonomous operation (and does not provide much of a standardized interface beyond some basic JAUS teleoperation commands) and "autonomize" it by using the existing interfaces wherever possible and adding new sensors when necessary to allow the robot to perform novesl tasks fully autonomously (work performed in collaboration with Indiana University Bloomington).

Assistive robots
Our software can be used to instruct robots a broad set of assistive tasks in various health and elder care settings. This brief demo video shows our system learning to perform a simple feeding task where a caregiver initially positions the arm to allow for food delivery to the subject. The robot also learns to recognize and manipulate carrots from instructions.

Service robots
Our software can be used to instruct a broad set of service tasks typical for the retail and hospitality industry, as well as many office settings. This brief demo video shows our system learning to perform a simple delivery task in a multi-robot system where a fixed-mounted arm works together with a mobile platform to jointly perform a delivery task.

Software Integration

We offer to integrate our software with existing robot platforms with standardized interfaces (e.g., ROS).

Component Adaptation

We offer to adapt our software components to specific customer requirements (e.g., adapting language and corresponding concepts to specific tasks).

Custom Software Configuration and Setup

We offer to configure, install, and set up our software at the customer site to ensure everything works as expected.

Software Subscriptions and Updates

We offer different models for obtaining software updates, through one-time upgrade fees, or automatic upgrades through annual subscriptions.

Matthias Scheutz, Ph.D, Ph.D. is President and CEO of Thinking Robots, Inc.

He has extensive experience in designing and implementing integrated cognitive robotic architectures that can interact with humans in natural language and quickly acquire new knowledge from natural language instructions.

He is also the Director of the Tufts Human-Robot Interaction Laboratory and an expert in evaluating robot systems in rigorous human-robot interaction experiments.

Bradley Oosterveld, MA, is CTO of Thinking Robots, Inc.

He has extensive experience with all aspects of natural-language enabled robots, cognitive robotic architectures, and robot middleware.

Prior to joining Thinking Robots, Inc., Bradley was a senior research staff member at the Tufts Human-Robot Interaction Laboratory.