StanfordVL/OmniGibson
Oct 2024 – May 2025
8 Months active
Languages Used
PythonYAMLDockerfilePyTorchC++BDDLGLSLJSON
Technical Skills
CI/CDCollision DetectionControl SystemsPython PackagingRoboticsSimulation
Chengshu Li
PROFILE
Chengshu Li
Over eight months, contributed to the StanfordVL/OmniGibson repository by building and refining robotics simulation features, motion planning pipelines, and robust data generation workflows. Leveraged Python and PyTorch to implement unified robot control APIs, enhance collision detection, and optimize sampling for scalable task coverage. Focused on simulation realism and reliability, the work included developing joint control interfaces, stabilizing CI/CD pipelines, and expanding task sets using BDDL and custom configuration management. Addressed core stability and compatibility issues through disciplined debugging and code refactoring, resulting in safer, more efficient sampling and deployment. Maintained code clarity with thorough documentation and targeted test automation.
Overall Statistics
Feature vs Bugs
54%Features
Repository Contributions
156Total
Bugs
37
Commits
156
Features
43
Lines of code
671,650
Activity Months8
Your Network
137 peopleShared Repositories
24
Work History
May 2025
37 Commits • 10 Features
May 1, 2025May 2025 development for StanfordVL/OmniGibson focused on safe, scalable sampling workflows, stability, and expanded task coverage. Key features delivered include manual sampling mode with safe traversal safeguards (commits 6ddf0f95a934c6224863bc11894435bac826bcbc; 75e87ea306ddefd6bdeeaf98466b2f3aa9b7262e), and a new task added to the B30 task set (commit 1298f62f1ac3f978ed87088f736aafd593856ab2). Toy placement definition refined to include only toy figures (commit c5b53f1f365ff10666506c0db3a7a0cee44c9fbe), and GPU dynamics disabled to improve stability and performance (commit 4db92a262bf09cda074f94961776ac94ad5943ac). Major bug fixes include B30 core stability fixes addressing seg map and BDDL issues across multiple commits (d432558f56aa36363bf23a8060418880897f96ae; 6e52e6f70ad85f86f1bbc6e289ccb584c14d87e1; 8b6fb01a56c2243168ef97d20ee256c52f1dbe42; bedf047ab89209d76c4ef71f16d89ea008b2de30; 07cec87cd749c44310b65ee6250fca057f69183d; 854577cd41abc8e3831228e1b6fb96fc448b632f), and sampling behavior improvements (do not perform last-ditch sampling for ontop floor; fix attached state sampling) (e9e2de74a6886058be9660f379827f3afd086d8a; e19166354fd0902748ca4cde92c2a46813379586). Additional fixes include whitelist not None enforcement (beb31e85e653e99e1dd40e1d3e72432ad2ad75d7) and various BDDL/traversal fixes (7eeaf329892982265961256536974b9616bca698; 568b8b8b5259216eb4ddb4bdddf849499ad6566c; 99490e02eaf974516f7136194003fead274451dc). The month also delivered expanded task coverage and tooling: Additional B30 tasks (54db917a70461d34d1ab70c0126a61f52bb48f06; 80ffed259cb1b95667bbed31f3f3423d8a48d577), Sampling Tools and Enhancements (f8fad6cda0199581463fb6b6a3c7761412700718; da33dceb0a4b306134c19c066ce7a2d402e68069), B30 Task Expansions (17ad9d53fad290cbaec6cb806c3d138fbe981be7; ed8f87e64303b29152ed29a2ecdb494d7bcc96e5; db921b6d6b205beaadfa9d968edc4ce661c72179; 8612d1182c376507e329256fa756bac189e93ece; c7a0c25aa5baf5d06f0fc324a6296602ea6a7bbb), and B31-50 Task Set Enhancements and BDDL Update (f26ed21b96fded5947e2e69bafac9a35ce8e03d5; 7eb47a652d2a8f0ee02994cea6b3752cce3407fb; cac31f4c80e84865b1dba9b0dddbf2391de43d0d; ebcc698563f684c66d5f42dcb68dabfbe19da928; 6029de526ae71a00aa7571f8734bab5d74f3781a). In addition, there were updates to Chengshu task custom lists (d7c638077d822d3dff90362d9635c25fbdfcea40; cb680540ff084a0b720560c8cc6516f4804ae10f), and whitelist enforcement to non-None and related JSON updates. Overall, May 2025 delivered measurable business value through safer data collection, more robust core stability for B30, expanded task coverage through B30-B50, and improved tooling for sampling. Technologies and skills demonstrated include risk-aware sampling, BDDL and ground-truth traversal fixes, scripting and automation for sampling workflows, JSON configuration/data updates, and disciplined version control across multiple feature sets.
37 Commits • 10 Features
May 1, 2025May 2025 development for StanfordVL/OmniGibson focused on safe, scalable sampling workflows, stability, and expanded task coverage. Key features delivered include manual sampling mode with safe traversal safeguards (commits 6ddf0f95a934c6224863bc11894435bac826bcbc; 75e87ea306ddefd6bdeeaf98466b2f3aa9b7262e), and a new task added to the B30 task set (commit 1298f62f1ac3f978ed87088f736aafd593856ab2). Toy placement definition refined to include only toy figures (commit c5b53f1f365ff10666506c0db3a7a0cee44c9fbe), and GPU dynamics disabled to improve stability and performance (commit 4db92a262bf09cda074f94961776ac94ad5943ac). Major bug fixes include B30 core stability fixes addressing seg map and BDDL issues across multiple commits (d432558f56aa36363bf23a8060418880897f96ae; 6e52e6f70ad85f86f1bbc6e289ccb584c14d87e1; 8b6fb01a56c2243168ef97d20ee256c52f1dbe42; bedf047ab89209d76c4ef71f16d89ea008b2de30; 07cec87cd749c44310b65ee6250fca057f69183d; 854577cd41abc8e3831228e1b6fb96fc448b632f), and sampling behavior improvements (do not perform last-ditch sampling for ontop floor; fix attached state sampling) (e9e2de74a6886058be9660f379827f3afd086d8a; e19166354fd0902748ca4cde92c2a46813379586). Additional fixes include whitelist not None enforcement (beb31e85e653e99e1dd40e1d3e72432ad2ad75d7) and various BDDL/traversal fixes (7eeaf329892982265961256536974b9616bca698; 568b8b8b5259216eb4ddb4bdddf849499ad6566c; 99490e02eaf974516f7136194003fead274451dc). The month also delivered expanded task coverage and tooling: Additional B30 tasks (54db917a70461d34d1ab70c0126a61f52bb48f06; 80ffed259cb1b95667bbed31f3f3423d8a48d577), Sampling Tools and Enhancements (f8fad6cda0199581463fb6b6a3c7761412700718; da33dceb0a4b306134c19c066ce7a2d402e68069), B30 Task Expansions (17ad9d53fad290cbaec6cb806c3d138fbe981be7; ed8f87e64303b29152ed29a2ecdb494d7bcc96e5; db921b6d6b205beaadfa9d968edc4ce661c72179; 8612d1182c376507e329256fa756bac189e93ece; c7a0c25aa5baf5d06f0fc324a6296602ea6a7bbb), and B31-50 Task Set Enhancements and BDDL Update (f26ed21b96fded5947e2e69bafac9a35ce8e03d5; 7eb47a652d2a8f0ee02994cea6b3752cce3407fb; cac31f4c80e84865b1dba9b0dddbf2391de43d0d; ebcc698563f684c66d5f42dcb68dabfbe19da928; 6029de526ae71a00aa7571f8734bab5d74f3781a). In addition, there were updates to Chengshu task custom lists (d7c638077d822d3dff90362d9635c25fbdfcea40; cb680540ff084a0b720560c8cc6516f4804ae10f), and whitelist enforcement to non-None and related JSON updates. Overall, May 2025 delivered measurable business value through safer data collection, more robust core stability for B30, expanded task coverage through B30-B50, and improved tooling for sampling. Technologies and skills demonstrated include risk-aware sampling, BDDL and ground-truth traversal fixes, scripting and automation for sampling workflows, JSON configuration/data updates, and disciplined version control across multiple feature sets.
May 2025
April 2025
13 Commits • 4 Features
Apr 1, 2025April 2025 summary for StanfordVL/OmniGibson focusing on realism, robustness, and data generation improvements to accelerate simulation-based development and validation. Key work concentrated on tightening environmental context for targeted tasks, expanding and hardening the datagen pipeline, stabilizing particle systems, and improving sampling and asset-compatibility behavior to reduce crashes and improve reliability for downstream training and evaluation.
April 2025
13 Commits • 4 Features
Apr 1, 2025April 2025 summary for StanfordVL/OmniGibson focusing on realism, robustness, and data generation improvements to accelerate simulation-based development and validation. Key work concentrated on tightening environmental context for targeted tasks, expanding and hardening the datagen pipeline, stabilizing particle systems, and improving sampling and asset-compatibility behavior to reduce crashes and improve reliability for downstream training and evaluation.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered R1Pro robot support with velocity control parameters and fixed collision data handling for R1 robots in OmniGibson. These changes improve simulation realism, safety, and maintainability, and broaden platform compatibility for R1Pro. Maintained code clarity with targeted comments to support future maintenance.
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered R1Pro robot support with velocity control parameters and fixed collision data handling for R1 robots in OmniGibson. These changes improve simulation realism, safety, and maintainability, and broaden platform compatibility for R1Pro. Maintained code clarity with targeted comments to support future maintenance.
March 2025
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 — OmniGibson (StanfordVL/OmniGibson) monthly summary focused on cross-robot API consistency, robust motion and collision handling, and enhanced manipulation capabilities. Delivered changes improve developer productivity, integration with new robot variants, and simulation reliability, unlocking faster feature delivery and safer robotics iterations.
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025 — OmniGibson (StanfordVL/OmniGibson) monthly summary focused on cross-robot API consistency, robust motion and collision handling, and enhanced manipulation capabilities. Delivered changes improve developer productivity, integration with new robot variants, and simulation reliability, unlocking faster feature delivery and safer robotics iterations.
January 2025
35 Commits • 13 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for StanfordVL/OmniGibson. This period delivered a robust joint control interface with action mapping, improved physics timing reliability, and substantial maintainability enhancements. Key features and refinements were implemented with clear commit intent, and the team focused on reliability, performance, and developer productivity to enable production-readiness of the robot primitives.
35 Commits • 13 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for StanfordVL/OmniGibson. This period delivered a robust joint control interface with action mapping, improved physics timing reliability, and substantial maintainability enhancements. Key features and refinements were implemented with clear commit intent, and the team focused on reliability, performance, and developer productivity to enable production-readiness of the robot primitives.
January 2025
December 2024
19 Commits • 2 Features
Dec 1, 2024December 2024 saw focused delivery across OmniGibson, delivering measurable business value through test automation improvements, robust CuRobo motion planning, and bug fixes that stabilize end-to-end behavior. The work reduced test feedback loops, hardened robotics workflows, and improved the reliability of demos and deployments while expanding the capabilities of the CuRobo stack for IK-first planning and trajectory handling.
December 2024
19 Commits • 2 Features
Dec 1, 2024December 2024 saw focused delivery across OmniGibson, delivering measurable business value through test automation improvements, robust CuRobo motion planning, and bug fixes that stabilize end-to-end behavior. The work reduced test feedback loops, hardened robotics workflows, and improved the reliability of demos and deployments while expanding the capabilities of the CuRobo stack for IK-first planning and trajectory handling.
November 2024
42 Commits • 9 Features
Nov 1, 2024Month: 2024-11. Focused on delivering robust curobo features, stabilizing test and CI, and hardening CUDA/PyTorch compatibility for cross-environment deployments. Key efforts included API-unified curobo examples, enhanced AG grasp control, collision-aware rollout performance improvements, and CI-friendly environment/configuration updates. These efforts improve reliability, scalability, and business value by enabling faster iteration, safer multi-robot demos, and smoother CI runs.
42 Commits • 9 Features
Nov 1, 2024Month: 2024-11. Focused on delivering robust curobo features, stabilizing test and CI, and hardening CUDA/PyTorch compatibility for cross-environment deployments. Key efforts included API-unified curobo examples, enhanced AG grasp control, collision-aware rollout performance improvements, and CI-friendly environment/configuration updates. These efforts improve reliability, scalability, and business value by enabling faster iteration, safer multi-robot demos, and smoother CI runs.
November 2024
October 2024
3 Commits • 2 Features
Oct 1, 2024October 2024 - OmniGibson (StanfordVL/OmniGibson) focused on resource efficiency, CI/CD improvements, and flexible control tuning. Delivered three key changes across the repository that improve runtime performance, test reliability, and configurability for motion/embodiment scenarios.
October 2024
3 Commits • 2 Features
Oct 1, 2024October 2024 - OmniGibson (StanfordVL/OmniGibson) focused on resource efficiency, CI/CD improvements, and flexible control tuning. Delivered three key changes across the repository that improve runtime performance, test reliability, and configurability for motion/embodiment scenarios.
Activity
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Quality Metrics
Correctness83.6%
Maintainability85.6%
Architecture80.6%
Performance75.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
BDDLC++DockerfileGLSLJSONN/APyTorchPythonYAML
Technical Skills
3D Graphics3D Rendering3D Simulation3D TransformationsAIAI BenchmarkingAI PlanningAI/MLAPI DesignAPI DevelopmentAPI IntegrationAPI RefactoringAction PrimitivesActivity DefinitionBDDL
Repositories Contributed To
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