ECLAIR-Robotics/crackle
Oct 2024 – Feb 2026
14 Months active
Languages Used
PythonYAMLC++CMakeShellXacroBashGit
Technical Skills
3D PerceptionComputer VisionObject DetectionROS2Robotic Arm ControlRobotics
Tanay Garg
PROFILE
Tanay Garg
Tanay Garg developed advanced robotics automation features for the ECLAIR-Robotics/crackle repository, focusing on perception-to-action pipelines, conversational AI, and robust manipulation workflows. He integrated YOLO-based object detection with 3D localization and robotic arm control, leveraging Python and ROS2 to synchronize image and point cloud data for precise manipulation. Tanay also implemented LLM-driven planning, wake-word detection, and emotion-aware interfaces, combining machine learning with real-time audio and vision processing. His work included modular API development, simulation tooling, and CI/CD automation, resulting in a maintainable, scalable robotics stack. The engineering depth addressed reliability, cross-environment portability, and streamlined onboarding for future contributors.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
62Total
Bugs
3
Commits
62
Features
36
Lines of code
84,508
Activity Months14
Your Network
57 people
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ECLAIR-Robotics/crackle: Delivered a key feature enhancement to the Vision Server and Planning Components by introducing new model files and updating existing model paths to enable more accurate perception and planning. This work was realized through a focused commit that adds models and optimizes the model-loading workflow. No major bugs were reported this month. The changes raise the reliability of real-time perception and planning, streamline future model iterations, and strengthen integration between the vision server and planning components. Demonstrated capabilities in model management, path configuration, and end-to-end feature integration within the robotics stack.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ECLAIR-Robotics/crackle: Delivered a key feature enhancement to the Vision Server and Planning Components by introducing new model files and updating existing model paths to enable more accurate perception and planning. This work was realized through a focused commit that adds models and optimizes the model-loading workflow. No major bugs were reported this month. The changes raise the reliability of real-time perception and planning, streamline future model iterations, and strengthen integration between the vision server and planning components. Demonstrated capabilities in model management, path configuration, and end-to-end feature integration within the robotics stack.
February 2026
January 2026
1 Commits
Jan 1, 2026January 2026: Delivered a portability fix for configuration and script paths in ECLAIR-Robotics/crackle, replacing hardcoded absolute paths with environment-agnostic paths to improve reliability across diverse setups. This change reduces environment-specific failures and eases deployment for users in mixed environments. Commit c01f9b61aaf4e1c14d965e381dffd05c4154395b (message: changed absolute paths).
January 2026
1 Commits
Jan 1, 2026January 2026: Delivered a portability fix for configuration and script paths in ECLAIR-Robotics/crackle, replacing hardcoded absolute paths with environment-agnostic paths to improve reliability across diverse setups. This change reduces environment-specific failures and eases deployment for users in mixed environments. Commit c01f9b61aaf4e1c14d965e381dffd05c4154395b (message: changed absolute paths).
December 2025
5 Commits • 2 Features
Dec 1, 2025In December 2025, two major capabilities were delivered for the ECLAIR-Robotics/crackle project, advancing hands-free robot control and autonomous task execution. The AI-driven Command Processing and Executable Code Generation feature consolidates enhanced robot interaction, refined object manipulation, and automatic translation of user commands into executable Python actions. The Wake Word Detection and Audio-Driven Action Execution feature adds a TensorFlow Lite wake-word model and dynamic, audio-triggered actions within Crackle's planning system. Together, these workstreams reduce manual coding, accelerate feature delivery, and strengthen the end-to-end automation pipeline for robotics tasks. The work demonstrates strong AI/ML integration with robotics, improved user experience, and a scalable foundation for future features.
5 Commits • 2 Features
Dec 1, 2025In December 2025, two major capabilities were delivered for the ECLAIR-Robotics/crackle project, advancing hands-free robot control and autonomous task execution. The AI-driven Command Processing and Executable Code Generation feature consolidates enhanced robot interaction, refined object manipulation, and automatic translation of user commands into executable Python actions. The Wake Word Detection and Audio-Driven Action Execution feature adds a TensorFlow Lite wake-word model and dynamic, audio-triggered actions within Crackle's planning system. Together, these workstreams reduce manual coding, accelerate feature delivery, and strengthen the end-to-end automation pipeline for robotics tasks. The work demonstrates strong AI/ML integration with robotics, improved user experience, and a scalable foundation for future features.
December 2025
November 2025
6 Commits • 6 Features
Nov 1, 2025Concise monthly summary for 2025-11 focusing on delivering end-to-end Crackle Robotics System enhancements. Achievements include startup automation, perceptual capabilities, interactive behavior, and voice-enabled interactions, with emphasis on reliability and business value.
November 2025
6 Commits • 6 Features
Nov 1, 2025Concise monthly summary for 2025-11 focusing on delivering end-to-end Crackle Robotics System enhancements. Achievements include startup automation, perceptual capabilities, interactive behavior, and voice-enabled interactions, with emphasis on reliability and business value.
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for ECLAIR-Robotics/crackle: Delivered foundational autonomy and wake-word capabilities. Implemented Crackle Robot Finite State Machine Core to manage IDs, tasks, listening, resetting, and failure states with coordinated scanning/listening; established planning hooks to enable task-driven behavior. Integrated wake-word detection using openWakeWord, wired in audio input streams and wake-word models, and updated the FSM to respond to wake-word events. Added GitHub Actions workflows for build, publish, and test of the openWakeWord component, enabling automated CI/CD. Impact: higher autonomy, improved user interaction readiness, and faster, safer deployments via automated quality checks. Collaboration: multiple co-authors contributed to wake-word work. Technologies: FSM, robotics control, audio processing, openWakeWord, CI/CD with GitHub Actions.
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for ECLAIR-Robotics/crackle: Delivered foundational autonomy and wake-word capabilities. Implemented Crackle Robot Finite State Machine Core to manage IDs, tasks, listening, resetting, and failure states with coordinated scanning/listening; established planning hooks to enable task-driven behavior. Integrated wake-word detection using openWakeWord, wired in audio input streams and wake-word models, and updated the FSM to respond to wake-word events. Added GitHub Actions workflows for build, publish, and test of the openWakeWord component, enabling automated CI/CD. Impact: higher autonomy, improved user interaction readiness, and faster, safer deployments via automated quality checks. Collaboration: multiple co-authors contributed to wake-word work. Technologies: FSM, robotics control, audio processing, openWakeWord, CI/CD with GitHub Actions.
October 2025
September 2025
17 Commits • 6 Features
Sep 1, 2025September 2025 (2025-09) delivered a robust MoveIt! based manipulation pipeline for Crackle, enhanced data capture and experimentation tooling, and reinforced setup and maintenance practices to accelerate contributor onboarding and reduce operational risk. The month focused on delivering business value through reliable robotics automation, reproducible experimentation, and maintainable code. Key outcomes include a MoveIt! manipulation integration with perception-driven planning, energy-efficient and robust grasp pose generation, and reliable pickup services; foundational ROS bag recording/playback tooling for experiment data capture; flexible launch configurations enabling smooth operation across simulation and real hardware; comprehensive documentation and onboarding automation to ease contributor ramp-up; and targeted code cleanup and PlannerAPI/ROS stability improvements to improve maintainability and reliability.
September 2025
17 Commits • 6 Features
Sep 1, 2025September 2025 (2025-09) delivered a robust MoveIt! based manipulation pipeline for Crackle, enhanced data capture and experimentation tooling, and reinforced setup and maintenance practices to accelerate contributor onboarding and reduce operational risk. The month focused on delivering business value through reliable robotics automation, reproducible experimentation, and maintainable code. Key outcomes include a MoveIt! manipulation integration with perception-driven planning, energy-efficient and robust grasp pose generation, and reliable pickup services; foundational ROS bag recording/playback tooling for experiment data capture; flexible launch configurations enabling smooth operation across simulation and real hardware; comprehensive documentation and onboarding automation to ease contributor ramp-up; and targeted code cleanup and PlannerAPI/ROS stability improvements to improve maintainability and reliability.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 summary for ECLAIR-Robotics/crackle: Delivered end-to-end enhancements to conversational AI and robotics control, integrating emotion-aware interfaces and control primitives with planner-driven orchestration. Implemented API methods for setting emotion and gripper control; enabled LLM-based conversational capabilities with emotion parsing; integrated speech recognition, text-to-speech, and robot arm control into the planner; refactored legacy vision processing to support the new perceptual and control loops. Documentation tasks identified and tracked for the upcoming sprint.
1 Commits • 1 Features
Jun 1, 2025June 2025 summary for ECLAIR-Robotics/crackle: Delivered end-to-end enhancements to conversational AI and robotics control, integrating emotion-aware interfaces and control primitives with planner-driven orchestration. Implemented API methods for setting emotion and gripper control; enabled LLM-based conversational capabilities with emotion parsing; integrated speech recognition, text-to-speech, and robot arm control into the planner; refactored legacy vision processing to support the new perceptual and control loops. Documentation tasks identified and tracked for the upcoming sprint.
June 2025
April 2025
9 Commits • 5 Features
Apr 1, 2025April 2025 highlights for ECLAIR-Robotics/crackle: Reorganized MoveIt Task Constructor into a dedicated subdirectory, introduced CrackleFace ROS node and an emotion simulation prototype (via raylib), and updated CI/format/lint configurations. Cleaned and restructured CI/CD pipelines by removing outdated GitHub Actions workflows related to CI, formatting, and pre-release checks. Removed the emotion_raylib integration to streamline the codebase and reduce dependencies. Delivered foundational MoveIt integration for robot arm control, object detection/segmentation, and point cloud processing pipelines, including cleanup of obsolete point cloud code. Improved repository hygiene by expanding .gitignore to exclude virtual environments and generated artifacts (.pt, .pth), and merged related diffs."
April 2025
9 Commits • 5 Features
Apr 1, 2025April 2025 highlights for ECLAIR-Robotics/crackle: Reorganized MoveIt Task Constructor into a dedicated subdirectory, introduced CrackleFace ROS node and an emotion simulation prototype (via raylib), and updated CI/format/lint configurations. Cleaned and restructured CI/CD pipelines by removing outdated GitHub Actions workflows related to CI, formatting, and pre-release checks. Removed the emotion_raylib integration to streamline the codebase and reduce dependencies. Delivered foundational MoveIt integration for robot arm control, object detection/segmentation, and point cloud processing pipelines, including cleanup of obsolete point cloud code. Improved repository hygiene by expanding .gitignore to exclude virtual environments and generated artifacts (.pt, .pth), and merged related diffs."
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ECLAIR-Robotics/crackle. Key feature delivered: MoveIt scene updater integration enabling perception-to-manipulation capabilities. The work includes integration of a RealSense camera and a gripper with the MoveIt scene updater, addition of the octomap_server2 submodule, and updates to URDF and CMakeLists to support these components. The associated commit 749c095bb060808cee8492db7f7550ee8db58188 captures the core change.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for ECLAIR-Robotics/crackle. Key feature delivered: MoveIt scene updater integration enabling perception-to-manipulation capabilities. The work includes integration of a RealSense camera and a gripper with the MoveIt scene updater, addition of the octomap_server2 submodule, and updates to URDF and CMakeLists to support these components. The associated commit 749c095bb060808cee8492db7f7550ee8db58188 captures the core change.
March 2025
February 2025
11 Commits • 5 Features
Feb 1, 2025February 2025 monthly summary for ECLAIR-Robotics/crackle. Delivered foundational features for robot-assisted drawing and expanded hardware capabilities, while improving developer workflow and repository stability. The work enhances automation, simulation fidelity, and onboarding efficiency, translating into faster feature delivery and reduced maintenance costs.
February 2025
11 Commits • 5 Features
Feb 1, 2025February 2025 monthly summary for ECLAIR-Robotics/crackle. Delivered foundational features for robot-assisted drawing and expanded hardware capabilities, while improving developer workflow and repository stability. The work enhances automation, simulation fidelity, and onboarding efficiency, translating into faster feature delivery and reduced maintenance costs.
January 2025
2 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for ECLAIR-Robotics/crackle. Focused on stabilizing inter-service interactions, enhancing 3D perception synchronization, and enabling simulation workflows. Delivered two features: 3D Object Detection Synchronization and Inter-Service Testing Enhancements, and MoveIt Integration with URDF Enhancements. Fixed a synchronization issue in service callback paths; expanded test coverage with new service definitions and inter-service tests; updated repo submodules and introduced crackle_moveit package to support ROS2 simulation use cases. These efforts improve reliability, testing confidence, and readiness for ROS2-based deployment in robotic applications with accurate 3D perception and streamlined simulation workflows.
2 Commits • 2 Features
Jan 1, 2025January 2025 monthly summary for ECLAIR-Robotics/crackle. Focused on stabilizing inter-service interactions, enhancing 3D perception synchronization, and enabling simulation workflows. Delivered two features: 3D Object Detection Synchronization and Inter-Service Testing Enhancements, and MoveIt Integration with URDF Enhancements. Fixed a synchronization issue in service callback paths; expanded test coverage with new service definitions and inter-service tests; updated repo submodules and introduced crackle_moveit package to support ROS2 simulation use cases. These efforts improve reliability, testing confidence, and readiness for ROS2-based deployment in robotic applications with accurate 3D perception and streamlined simulation workflows.
January 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ECLAIR-Robotics/crackle: Delivered a major feature to enhance robot perception and pick-and-place capabilities. The work integrated camera intrinsic parameters and refined point cloud processing to improve object localization accuracy, and updated the robot driver configuration to enable vacuum gripper operation for automated pick-and-place tasks. No major bugs fixed in this period; ongoing verification and validation in simulation and field tests continued to ensure reliability. The feature is expected to improve automation throughput and reduce manual intervention, contributing to higher first-pass success rates and safer manipulation.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for ECLAIR-Robotics/crackle: Delivered a major feature to enhance robot perception and pick-and-place capabilities. The work integrated camera intrinsic parameters and refined point cloud processing to improve object localization accuracy, and updated the robot driver configuration to enable vacuum gripper operation for automated pick-and-place tasks. No major bugs fixed in this period; ongoing verification and validation in simulation and field tests continued to ensure reliability. The feature is expected to improve automation throughput and reduce manual intervention, contributing to higher first-pass success rates and safer manipulation.
November 2024
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 — The Crackle project for ECLAIR-Robotics progressed by building a scalable autonomous planning stack with core features, ROS integration scaffolding, and LLM-based planning capabilities. The work focuses on enabling end-to-end automation, modularity, and testability, setting the foundation for faster delivery and safer robot behavior in future iterations. Key features delivered: - Crackle Planning Package and YOLO Perception Adjustments: Introduced a new planning package with initial node structure, updated YOLO segmentation node, intrinsic matrix adjustments, and average coordinate calculations; added a placeholder move_to_location for future robotic arm control. (Commits: 8f2eba46366cafc032f7394fbc06ea4df188da49) - ROS Interface and Planner API Scaffolding: Added RosInterface in planner_lib to enable ROS integration; created PlannerAPI class skeleton and adjusted PlannerNode for future ROS interactions and robot manipulation tasks. (Commits: 2945c098e24193277befd70890a7ef7b4750a66f, ab693242e78f9e99b8194870077232d91192f7f5) - LLM-Based Planning API (GptAPI) and Testing Utilities: Added GptAPI class to interface with an LLM for planning actions and placeholder command generation; adjusted yolo_segment_node for arm movement/service waiting; included pointing_testing.py and prompt.txt for testing pointing gestures. (Commit: 5252872e84a343930e6696282f7ab00234765329) Major bugs fixed: - No critical defects reported this month. The focus was on groundwork and compatibility work to reduce future defect risk and enable reliable integrations (ROS interface, LLM-based planning, and perception adjustments). Overall impact and accomplishments: - Delivered a cohesive foundation for an end-to-end autonomous planning workflow, combining perception, planning, and ROS integration. This enables faster iteration, safer robot behavior, and clearer interfaces for future enhancements and maintenance. - Establishes modular APIs and testing utilities to accelerate feature development and verification across the Crackle stack. Technologies/skills demonstrated: - ROS integration and planning API scaffolding (Python-based modules) - LLM-based planning interface and testing utilities - Computer vision integration and perception adjustments (YOLO) with calibration considerations (intrinsic matrix, coordinate averaging) - Software architecture for modular, testable robotics components
4 Commits • 3 Features
Nov 1, 2024Month: 2024-11 — The Crackle project for ECLAIR-Robotics progressed by building a scalable autonomous planning stack with core features, ROS integration scaffolding, and LLM-based planning capabilities. The work focuses on enabling end-to-end automation, modularity, and testability, setting the foundation for faster delivery and safer robot behavior in future iterations. Key features delivered: - Crackle Planning Package and YOLO Perception Adjustments: Introduced a new planning package with initial node structure, updated YOLO segmentation node, intrinsic matrix adjustments, and average coordinate calculations; added a placeholder move_to_location for future robotic arm control. (Commits: 8f2eba46366cafc032f7394fbc06ea4df188da49) - ROS Interface and Planner API Scaffolding: Added RosInterface in planner_lib to enable ROS integration; created PlannerAPI class skeleton and adjusted PlannerNode for future ROS interactions and robot manipulation tasks. (Commits: 2945c098e24193277befd70890a7ef7b4750a66f, ab693242e78f9e99b8194870077232d91192f7f5) - LLM-Based Planning API (GptAPI) and Testing Utilities: Added GptAPI class to interface with an LLM for planning actions and placeholder command generation; adjusted yolo_segment_node for arm movement/service waiting; included pointing_testing.py and prompt.txt for testing pointing gestures. (Commit: 5252872e84a343930e6696282f7ab00234765329) Major bugs fixed: - No critical defects reported this month. The focus was on groundwork and compatibility work to reduce future defect risk and enable reliable integrations (ROS interface, LLM-based planning, and perception adjustments). Overall impact and accomplishments: - Delivered a cohesive foundation for an end-to-end autonomous planning workflow, combining perception, planning, and ROS integration. This enables faster iteration, safer robot behavior, and clearer interfaces for future enhancements and maintenance. - Establishes modular APIs and testing utilities to accelerate feature development and verification across the Crackle stack. Technologies/skills demonstrated: - ROS integration and planning API scaffolding (Python-based modules) - LLM-based planning interface and testing utilities - Computer vision integration and perception adjustments (YOLO) with calibration considerations (intrinsic matrix, coordinate averaging) - Software architecture for modular, testable robotics components
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024In Oct 2024, delivered an end-to-end perception-to-action capability for ECLAIR-Robotics/crackle by integrating a YOLO-based object detection pipeline with 3D localization and robotic-arm manipulation. The system subscribes to synchronized image and point cloud streams, transforms detected points into the robot base frame, and commands the arm to move to the detected object's location. Implemented a camera-to-RealSense frame mapping to support robust 3D localization. This work lays the groundwork for autonomous manipulation workflows and reduces reliance on manual intervention.
October 2024
1 Commits • 1 Features
Oct 1, 2024In Oct 2024, delivered an end-to-end perception-to-action capability for ECLAIR-Robotics/crackle by integrating a YOLO-based object detection pipeline with 3D localization and robotic-arm manipulation. The system subscribes to synchronized image and point cloud streams, transforms detected points into the robot base frame, and commands the arm to move to the detected object's location. Implemented a camera-to-RealSense frame mapping to support robust 3D localization. This work lays the groundwork for autonomous manipulation workflows and reduces reliance on manual intervention.
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Quality Metrics
Correctness83.2%
Maintainability83.8%
Architecture81.0%
Performance74.4%
AI Usage32.6%
Skills & Technologies
Programming Languages
AssemblyBashCC++CMakeDockerfileGitGit ConfigurationJSONMarkdown
Technical Skills
3D Modeling3D PerceptionAIAI IntegrationAI integrationAI/MLAPI DevelopmentAPI IntegrationAPI integrationAsynchronous ProgrammingAudio ProcessingBash ScriptingBuild SystemBuild SystemsC
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