The Kindalive project by Drew Smith uses a simulated dot-matrix face to express emotions through the modeling of eight key neurochemicals like dopamine and cortisol. Rather than using discrete sentiment labels via LLMs, this approach simulates the organic decay and interplay between chemicals to create fluid mood changes. These emotional states are visually represented through twelve facial movements based on the Facial Action Coding System (FACS).
- Models short-term and long-term moods through neurochemical simulation
- Uses nonverbal communication to increase robot relatability
- Employs 12 elements of the Facial Action Coding System for expression
- Models short-term and long-term moods through neurochemical simulation
- Uses nonverbal communication to increase robot relatability
- Employs 12 elements of the Facial Action Coding System for expression
A wireless 24GHz mmWave motion tracking system consisting of a standalone radar node that reports detected targets over Wi-Fi to a separate display unit.
Key details:
- Multi-target tracking for up to 3 people within a 7m range
- Live radar-style visualization on an Arduino GIGA Display Shield
- Distance-reactive audio alerts via a passive piezo buzzer
- Battery-powered operation with USB-C recharging and LED level indicators
- Includes 3D-printable enclosure files and full firmware for the XIAO ESP32-S3 and GIGA R1
Key details:
- Multi-target tracking for up to 3 people within a 7m range
- Live radar-style visualization on an Arduino GIGA Display Shield
- Distance-reactive audio alerts via a passive piezo buzzer
- Battery-powered operation with USB-C recharging and LED level indicators
- Includes 3D-printable enclosure files and full firmware for the XIAO ESP32-S3 and GIGA R1
This research presents penPal, a novel handheld drawing tool designed to support dynamic mark-making and creative decisions at the texture level. By integrating a tendon-driven continuum robot, the device allows its tip to move independently of the user's hand, providing an additional axis of control that combines manual manipulation with computer-assisted generation. Through studies involving participants and professional artists, the work examines how users negotiate multiple coordinate systems—including their own body, the paper, and the tool's tip—to construct complex compositions and explore generative potential through selective defamiliarization.
Key points:
* Use of tendon-driven continuum robots for handheld tools
* Texture-level creative control in drawing
* Negotiation of multiple coordinate systems during composition
* Integration of manual and computer-assisted mark-making
Key points:
* Use of tendon-driven continuum robots for handheld tools
* Texture-level creative control in drawing
* Negotiation of multiple coordinate systems during composition
* Integration of manual and computer-assisted mark-making
A collection of various LiDAR and ToF (Time-of-Flight) sensing solutions including 360 degree laser range scanners, depth cameras for ROS, and single-point ranging sensors. The selection features technologies such as DTOF, solid-state LiDAR, and RGB-D cameras suitable for SLAM and interactive AI applications.
Key product types:
RPLiDAR series (A1M8, A2M12, S2L, etc.)
STL DTOF sensors
CS depth cameras for ROS
TF series single-point ranging sensors
YDLIDAR linear array solid state LiDAR
Key product types:
RPLiDAR series (A1M8, A2M12, S2L, etc.)
STL DTOF sensors
CS depth cameras for ROS
TF series single-point ranging sensors
YDLIDAR linear array solid state LiDAR
These working notes by Russ Tedrake cover nonlinear dynamics and control with a specific focus on mechanical systems. The material explores how to achieve robust, efficient, and graceful robot movement through the integration of mechanical design, passive dynamics, and nonlinear control synthesis. Rather than relying solely on model-free approaches, the text emphasizes using the underlying structure of dynamical equations to develop more data-efficient and robust algorithms via optimization and machine learning.
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
NocKinematics is a modern, modular, and lightweight C++ Inverse Kinematics library designed specifically for Arduino and ESP32 microcontrollers. It utilizes the FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm to provide fast, iterative computations that are more efficient than traditional Jacobian Matrix approaches. The library is optimized for memory-constrained systems like AVR and ESP8266 by using specialized dynamic memory allocation to prevent RAM fragmentation.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
An introduction to JD Robotics and the Stewy V2 platform, designed as an approachable learning tool for robotics. The project emphasizes open-source development, simple yet capable hardware designs that maximize motor utility, and active community support for troubleshooting and customization.
Driven by labor shortages, Japan is leading the "Physical AI" sector by integrating AI with its advanced mechatronics and hardware expertise. Supported by significant government funding, the nation is moving from experimental trials to practical deployments in logistics, manufacturing, and defense, aiming for global market dominance by 2040.
This article introduces ROSA, a Robot Operating System (ROS) framework designed to seamlessly integrate Large Language Models (LLMs) into embodied AI systems. ROSA addresses the challenges of connecting LLMs to robotic hardware by providing a standardized interface for perception, planning, and action.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
The framework utilizes a prompt-based approach, converting robot tasks into natural language prompts for the LLM. This allows for flexible task specification and reasoning.
ROSA also includes tools for managing LLM outputs, ensuring safe and reliable robot behavior. The authors demonstrate ROSA’s capabilities through various experiments, showcasing its potential for creating more intelligent and adaptable robots.
This research introduces a novel robot operating system (ROS) framework designed to seamlessly integrate large language models (LLMs) into embodied artificial intelligence. The framework enables robots to interpret and execute natural language instructions with greater versatility and reliability.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
