bit-bots/bitbots_main
Oct 2024 – Mar 2026
2 Months active
Languages Used
PythonDSD
Technical Skills
Behavior TreesRoboticsTeam CoordinationAI programmingPythonPython programming
Clemens
PROFILE
Clemens
Worked on the bitbots_main repository to deliver robust behavior differentiation and game-state management for robotic soccer. Developed Python-based behavior trees and state machines to enable dynamic goalie and defender roles, integrating real-time goalie activity checks into the team’s decision logic. Enhanced game-state reliability by implementing standby modes, external control signals, and improved whistle-based transitions, using ROS for communication and control. Addressed robot stability during stopped states with animation-driven safeguards, reducing fall risk. Refined set-piece decision-making for corners and free kicks, improving in-game positioning. The work emphasized maintainable, safety-focused automation and clear state signaling, supporting both autonomous and manual interventions.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
4
Lines of code
534
Activity Months2
Your Network
23 peopleShared Repositories
23
Florian VahlMember
paerMember
bitbotsMember
Cornelius KruppMember
EamadMember
Florian VahlMember
Florian VahlMember
Jan GutscheMember
Hannes RichardtMember
Work History
March 2026
9 Commits • 3 Features
Mar 1, 2026Month: 2026-03 Overview: This month concentrated on strengthening game-state reliability, enabling external control, and refining in-game decision-making to improve automation safety and gameplay performance. Deliveries span robust game-state management, stability improvements during stopped states, enhanced set-piece decision-making, and sharper whistle-based game-flow transitions. Key features delivered: - Game State Management and External Control: Standby state support, new status and ball state publishing, integration of external stop signals for manual control, and updated game-state messaging. (Commits: ebea481d366d01e2ee453e4bf692e8226ca8ef61; 7e58a0d307c07918220dcf942491bcfb81c845ab; 397f833a69ecc5ce83d5be5ae03a5f7ab8d8b7c6; 7120046e4966892107b684237086ac66044dfba7) - Robot Stability in Stopped State: Fixed stability issues when entering stopped state, added animation-based stability, and improved state transitions to prevent falls. (Commits: a247bd7f952d8dbf41cd5fa6450222c836c33213; 11b4a5f8da2f6ff045ae0bd4ab063faea996fef2) - Set-Piece Decision-Making Improvements: Refined decision-making for corners, throw-ins, and rival free kicks to improve positioning and actions during gameplay. (Commits: 7ea9d6e2ae19fc6557e563c095b1c37aa87055ae; 4ff3bd9c394c43d5d10e85517708b4ab8972d4c1) - Whistle Detection and Game Flow Enhancement: Enhanced whistle detection to localize during state transitions and ensure correct game-state updates in response to whistle signals. (Commit: 84ebddd998eb9297b53355879fce94dde530993e) Major bugs fixed: - Robot Stability in Stopped State: Addressed instability and fall risk when transitioning to/staying in stopped state; updated transitions across HCM and body behavior modules to prevent falls. (Commits: a247bd7f952d8dbf41cd5fa6450222c836c33213; 11b4a5f8da2f6ff045ae0bd4ab063faea996fef2) Overall impact and accomplishments: - Increased reliability of game-state handling and external control, enabling safer manual interventions and clearer game-state signaling to downstream systems. - Reduced risk of robot falls during stop/start transitions, improving uptime and safety in real-game scenarios. - Improved in-game decision-making for set-pieces, leading to better positioning and more consistent scoring opportunities. - Sharper game-flow control through improved whistle localization, reducing mis-synchronization between sensors and gameplay state. Technologies/skills demonstrated: - Real-time game-state management, publish/subscribe patterns, and external control integration. - State-machine design, robust error handling, and safe transition policies. - Computer vision-assisted localization triggers (whistle-based cues) and localization-aware state transitions. - Algorithmic decision-making for football actions (corners, throw-ins, rival free kicks) and behavior-driven development. Business value: - More reliable automated gameplay with safer manual overrides, increasing team performance margins and reducing operational risk during matches and demonstrations.
9 Commits • 3 Features
Mar 1, 2026Month: 2026-03 Overview: This month concentrated on strengthening game-state reliability, enabling external control, and refining in-game decision-making to improve automation safety and gameplay performance. Deliveries span robust game-state management, stability improvements during stopped states, enhanced set-piece decision-making, and sharper whistle-based game-flow transitions. Key features delivered: - Game State Management and External Control: Standby state support, new status and ball state publishing, integration of external stop signals for manual control, and updated game-state messaging. (Commits: ebea481d366d01e2ee453e4bf692e8226ca8ef61; 7e58a0d307c07918220dcf942491bcfb81c845ab; 397f833a69ecc5ce83d5be5ae03a5f7ab8d8b7c6; 7120046e4966892107b684237086ac66044dfba7) - Robot Stability in Stopped State: Fixed stability issues when entering stopped state, added animation-based stability, and improved state transitions to prevent falls. (Commits: a247bd7f952d8dbf41cd5fa6450222c836c33213; 11b4a5f8da2f6ff045ae0bd4ab063faea996fef2) - Set-Piece Decision-Making Improvements: Refined decision-making for corners, throw-ins, and rival free kicks to improve positioning and actions during gameplay. (Commits: 7ea9d6e2ae19fc6557e563c095b1c37aa87055ae; 4ff3bd9c394c43d5d10e85517708b4ab8972d4c1) - Whistle Detection and Game Flow Enhancement: Enhanced whistle detection to localize during state transitions and ensure correct game-state updates in response to whistle signals. (Commit: 84ebddd998eb9297b53355879fce94dde530993e) Major bugs fixed: - Robot Stability in Stopped State: Addressed instability and fall risk when transitioning to/staying in stopped state; updated transitions across HCM and body behavior modules to prevent falls. (Commits: a247bd7f952d8dbf41cd5fa6450222c836c33213; 11b4a5f8da2f6ff045ae0bd4ab063faea996fef2) Overall impact and accomplishments: - Increased reliability of game-state handling and external control, enabling safer manual interventions and clearer game-state signaling to downstream systems. - Reduced risk of robot falls during stop/start transitions, improving uptime and safety in real-game scenarios. - Improved in-game decision-making for set-pieces, leading to better positioning and more consistent scoring opportunities. - Sharper game-flow control through improved whistle localization, reducing mis-synchronization between sensors and gameplay state. Technologies/skills demonstrated: - Real-time game-state management, publish/subscribe patterns, and external control integration. - State-machine design, robust error handling, and safe transition policies. - Computer vision-assisted localization triggers (whistle-based cues) and localization-aware state transitions. - Algorithmic decision-making for football actions (corners, throw-ins, rival free kicks) and behavior-driven development. Business value: - More reliable automated gameplay with safer manual overrides, increasing team performance margins and reducing operational risk during matches and demonstrations.
March 2026
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered goalie activity-aware behavior differentiation in bit-bots_main, enabling dynamic goalie vs defender decisions based on goalie activity state. Extended the team data capsule to include goalie activity status and integrated this check into the main behavior decision tree.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024: Delivered goalie activity-aware behavior differentiation in bit-bots_main, enabling dynamic goalie vs defender decisions based on goalie activity state. Extended the team data capsule to include goalie activity status and integrated this check into the main behavior decision tree.
Activity
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Quality Metrics
Correctness82.0%
Maintainability80.0%
Architecture80.0%
Performance78.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
DSDPython
Technical Skills
AI programmingBehavior TreesPythonPython programmingROSRoboticsTeam Coordinationanimationbehavioral programmingcommunication protocolscontrol systemsdecision treesgame developmentroboticsstate machine design
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