The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous–synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system’s communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.

TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

This paper provides a system overview about ANYmal, a quadrupedal robot developed for operation in harsh environments. The 30 kg, 0.5 m tall robotic dog was built in a modular way for simple mainte...

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ANYmal - toward legged robots for harsh environments

A chaotic path planning generator for autonomous mobile robots

Experimental investigation on coverage performance of a chaotic autonomous mobile robot

This article presents the hardware design and software algorithms of RoboSimian, a statically stable quadrupedal robot capable of both dexterous manipulation and versatile mobility in difficult terrain. The robot has generalized limbs and hands capable of mobility and manipulation, along with almost fully hemispherical three-dimensional sensing with passive stereo cameras. The system is semiautonomous, enabling low-bandwidth, high latency control operated from a standard laptop. Because limbs are used for mobility and manipulation, a single unified mobile manipulation planner is used to generate autonomous behaviors, including walking, sitting, climbing, grasping, and manipulating. The remote operator interface is optimized to designate, parametrize, sequence, and preview behaviors, which are then executed by the robot. RoboSimian placed fifth in the DARPA Robotics Challenge Trials, demonstrating its ability to perform disaster recovery tasks in degraded human environments.

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Mobile Manipulation and Mobility as Manipulation-Design and Algorithms of RoboSimian

The DARPA Robotics Challenge DRC program conducted a series of prize-based competition events to develop and demonstrate technology for disaster response. This article provides the official and definitive account of DRC Finals as the culmination of the DRC program. The article details the eight tasks Drive, Egress, Door, Valve, Wall, Surprise [Plug and Switch], Rubble [Obstacle or Debris], and Stairs constituting the Challenge, and describes how the competition encouraged supervised autonomous operation by intentionally degrading the communications channel between the remote human operators. The article presents the results of the DRC Finals and places those results in perspective by identifying both strengths and weaknesses of robot performance exhibited at the competition.

The DARPA Robotics Challenge Finals: Results and Perspectives

Research teams use common robots and machine learning to teach the robots outdoor navigation and locomotion skills.

How DARPA structures its robotics programs to improve locomotion and navigation

In November of 2005, the Defense Advanced Research Projects Agency initiated a new robotics program, Learning Locomotion, designed to solve some of the key outstanding issues. The expectation was that a combination of machine learning techniques and smart development would accelerate the pace of making autonomous legged systems robust and useful.

An overview of the Defense Advanced Research Projects Agency's Learning Locomotion program

An Overview of the DARPA Autonomous Robotic Manipulation (ARM) Program

The U.S. Defense Advanced Research Projects Agency's (DARPA) Neovision2 program aims to develop artificial vision systems based on the design principles employed by mammalian vision systems. Three such algorithms are briefly described in this paper. These neuromorphic-vision systems' performance in detecting objects in video was measured using a set of annotated clips. This paper describes the results of these evaluations including the data domains, metrics, methodologies, performance over a range of operating points and a comparison with computer vision based baseline algorithms.

Douglas Hackett

Papers

The DARPA Robotics Challenge Finals: Results and Perspectives

How DARPA structures its robotics programs to improve locomotion and navigation

An overview of the Defense Advanced Research Projects Agency's Learning Locomotion program

An Overview of the DARPA Autonomous Robotic Manipulation (ARM) Program

Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms