We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor and a low-gear-ratio transmission suitable for impedance and force control. We also present a novel foot contact sensor suitable for legged locomotion with hard impacts. A 2.2 kg quadruped robot with a large range of motion is assembled from eight identical actuator modules and four lower legs with foot contact sensors. Leveraging standard plastic 3D printing and off-the-shelf parts results in a lightweight and inexpensive robot, allowing for rapid distribution and duplication within the research community. We systematically characterize the achieved impedance at the foot in both static and dynamic scenarios, and measure a maximum dimensionless leg stiffness of 10.8 without active damping, which is comparable to the leg stiffness of a running human. Finally, to demonstrate the capabilities of the quadruped, we present a novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation. The controller can regulate complex motions while being robust to environmental uncertainty.

An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research

Multi-environment robotic transitions through adaptive morphogenesis

Numerous mobile robots in various forms specialize in either ground or aerial locomotion, whereas very few robots can perform complex locomotion tasks beyond simple walking and flying. We present t...

A bipedal walking robot that can fly, slackline, and skateboard.

This paper presents the design and implementation of a bounding controller for the MIT Cheetah 2 and its experimental results. The paper introduces the architecture of the controller along with the...

High-speed bounding with the MIT Cheetah 2: Control design and experiments

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to be useful assistants in human environments, because it involves tackling sub-problems from across robotics: perception, language understanding, navigation, and manipulation are all essential to OVMM. In addition, integration of the solutions to these sub-problems poses its own substantial challenges. To drive research in this area, we introduce the HomeRobot OVMM benchmark, where an agent navigates household environments to grasp novel objects and place them on target receptacles. HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch to encourage replication of real-world experiments across labs. We implement both reinforcement learning and heuristic (model-based) baselines and show evidence of sim-to-real transfer. Our baselines achieve a 20% success rate in the real world; our experiments identify ways future research work improve performance. See videos on our website: https://ovmm.github.io/.

pdf/homerobot-open-vocabulary-mobile-manipulation-34ndiwx2.pdf

HomeRobot: Open-Vocabulary Mobile Manipulation

This paper presents Stanford Doggo, a quasi-direct-drive quadruped capable of dynamic locomotion. This robot matches or exceeds common performance metrics of state-of-the-art legged robots. In terms of vertical jumping agility, a measure of average vertical speed, Stanford Doggo matches the best performing animal and surpasses the previous best robot by 22%. An overall design architecture is presented with focus on our quasi-direct-drive design methodology. The hardware and software to replicate this robot is open-source, requires only hand tools for manufacturing and assembly, and costs less than $3000.

Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

Traditional mathematics instruction serves many purposes in today ’ s education systems. Providing step-by-step guidance and lecture-style tuition in mathematics is often the norm. However, emerging insights into challenge, grit, and optimal teaching and learning strategies suggest that there may be benefit from increasing the mental burden on students. A short-term investigation was conducted into the use of choice, unstructured assessment and a “ locked box ” style of student-directed task for a high school calculus class of ESL students in China. Results were encouraging and suggested the emergence of a complex learning system composed of individual students that transcended the typical classroom experience.

https://www.conmaths.com/download/complexity-arising-from-escape-room-style-activities-in-a-high-school-calculus-class-11560.pdf

Complexity Arising from “Escape Room” Style Activities in A High School Calculus Class

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Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

Complexity Arising from “Escape Room” Style Activities in A High School Calculus Class