{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/wrap-to-pi","entry":"wrap_to_pi","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":3,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2603.23738","paper":"/paper/arxiv-2603-23738","title":"BXRL: Behavior-Explainable Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"HumanCompatibleAI/HighJax","path":"highjax/kinematics.py","file_url":"https://github.com/HumanCompatibleAI/HighJax/blob/HEAD/highjax/kinematics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0844c80d6d0b8c6f","mcp_get_code":{"code_sha256":"0844c80d6d0b8c6f"}},{"arxiv_id":"2502.12152","paper":"/paper/learning-getting-up-policies-for-real-world","title":"Learning Getting-Up Policies for Real-World Humanoid Robots","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RunpeiDong/HumanUP","path":"simulation/legged_gym/legged_gym/gym_utils/math.py","file_url":"https://github.com/RunpeiDong/HumanUP/blob/HEAD/simulation/legged_gym/legged_gym/gym_utils/math.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a25affa7af382246","mcp_get_code":{"code_sha256":"a25affa7af382246"}},{"arxiv_id":"2403.16015","paper":"/paper/mqe-unleashing-the-power-of-interaction-with","title":"MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment","date":null,"month_inferred_from_arxiv_id":"2024-03","title_source":"archive","repo":"ziyanx02/multiagent-quadruped-environment","path":"mqe/utils/math.py","file_url":"https://github.com/ziyanx02/multiagent-quadruped-environment/blob/HEAD/mqe/utils/math.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a25affa7af382246","mcp_get_code":{"code_sha256":"a25affa7af382246"}},{"arxiv_id":"2303.05760","paper":"/paper/gameformer-game-theoretic-modeling-and","title":"GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"MCZhi/GameFormer-Planner","path":"Planner/planner_utils.py","file_url":"https://github.com/MCZhi/GameFormer-Planner/blob/HEAD/Planner/planner_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a164c593eacff041","mcp_get_code":{"code_sha256":"a164c593eacff041"}},{"arxiv_id":"2206.14349","paper":"/paper/fleet-dagger-interactive-robot-fleet-learning","title":"Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision","date":"2022-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"berkeleyautomation/ifl_benchmark","path":"env/isaacgym/anymal_terrain.py","file_url":"https://github.com/berkeleyautomation/ifl_benchmark/blob/HEAD/env/isaacgym/anymal_terrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d111e1d4d2700dcf","mcp_get_code":{"code_sha256":"d111e1d4d2700dcf"}},{"arxiv_id":"1910.11432","paper":"/paper/hrl4in-hierarchical-reinforcement-learning","title":"HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators","date":"2019-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengshuLi/HRL4IN","path":"hrl4in/train_hrl_toy_env.py","file_url":"https://github.com/ChengshuLi/HRL4IN/blob/HEAD/hrl4in/train_hrl_toy_env.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"884d8918cd052a4b","mcp_get_code":{"code_sha256":"884d8918cd052a4b"}},{"arxiv_id":"1902.08705","paper":"/paper/a-general-framework-for-structured-learning","title":"A General Framework for Structured Learning of Mechanical Systems","date":"2019-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sisl/mechamodlearn","path":"mechamodlearn/utils.py","file_url":"https://github.com/sisl/mechamodlearn/blob/HEAD/mechamodlearn/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab3a6fcc0ae587bf","mcp_get_code":{"code_sha256":"ab3a6fcc0ae587bf"}}]}