{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:35T5PG3MV7HJZEIRQQCSRKO4SN","short_pith_number":"pith:35T5PG3M","canonical_record":{"source":{"id":"2108.06887","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-08-16T04:07:59Z","cross_cats_sorted":[],"title_canon_sha256":"fee7af856cc5e9c3e7b61f92fd8fc2fef57e0ebd9bcbd45bce1dc4dd00714b32","abstract_canon_sha256":"9480c30fe4d08ec11a8dd93535833e2d80979b671974e75d4b62a5e2ec675b3e"},"schema_version":"1.0"},"canonical_sha256":"df67d79b6cafce9c9111840528a9dc937427a35b59630e30453ff389c37127c2","source":{"kind":"arxiv","id":"2108.06887","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.06887","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"arxiv_version","alias_value":"2108.06887v2","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.06887","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_12","alias_value":"35T5PG3MV7HJ","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_16","alias_value":"35T5PG3MV7HJZEIR","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_8","alias_value":"35T5PG3M","created_at":"2026-07-05T03:07:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:35T5PG3MV7HJZEIRQQCSRKO4SN","target":"record","payload":{"canonical_record":{"source":{"id":"2108.06887","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-08-16T04:07:59Z","cross_cats_sorted":[],"title_canon_sha256":"fee7af856cc5e9c3e7b61f92fd8fc2fef57e0ebd9bcbd45bce1dc4dd00714b32","abstract_canon_sha256":"9480c30fe4d08ec11a8dd93535833e2d80979b671974e75d4b62a5e2ec675b3e"},"schema_version":"1.0"},"canonical_sha256":"df67d79b6cafce9c9111840528a9dc937427a35b59630e30453ff389c37127c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:52.152213Z","signature_b64":"GOSXjkYkZF/cOFQ91z02hudT4GWzA8iP34Q1rmqyHI+gSO9ouVL6FOGSLGwBKeJ5e+qXjnWJft30mS4IwWkEAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df67d79b6cafce9c9111840528a9dc937427a35b59630e30453ff389c37127c2","last_reissued_at":"2026-07-05T03:07:52.151717Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:52.151717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.06887","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:07:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TI3uAY0EHT6Kof0F5vUBrKeYK3mEIJHWjC+PibyjYiFl/XyyaxZiq2mCUVthGs/AFs0MlP7LRh5J0cvZ2ztSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:23:50.930747Z"},"content_sha256":"4a1ae51fa3a32995c97645a2b0fe5d126778f91e45b62cc64787259a2ed46b4c","schema_version":"1.0","event_id":"sha256:4a1ae51fa3a32995c97645a2b0fe5d126778f91e45b62cc64787259a2ed46b4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:35T5PG3MV7HJZEIRQQCSRKO4SN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Baocai Yin, Haiyin Piao, Jianchuan Ding, Lingping Gao, Wenxi Liu, Xin Yang, Yuxin Wang","submitted_at":"2021-08-16T04:07:59Z","abstract_excerpt":"Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e.g., tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.06887","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.06887/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:07:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AOU6G3H1qvXG2ZY7EAX3AHs3k9C9SE3eW4zNMgrp5n+yCgCAL9tBLq28ZZzwQRxEyZgGVx2VTyX3vK08gOLdAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:23:50.931264Z"},"content_sha256":"31a4ca75c3736641979c856a272fd227d3f80277f70fd72fe494f162fb9d4131","schema_version":"1.0","event_id":"sha256:31a4ca75c3736641979c856a272fd227d3f80277f70fd72fe494f162fb9d4131"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/bundle.json","state_url":"https://pith.science/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T01:23:50Z","links":{"resolver":"https://pith.science/pith/35T5PG3MV7HJZEIRQQCSRKO4SN","bundle":"https://pith.science/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/bundle.json","state":"https://pith.science/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/35T5PG3MV7HJZEIRQQCSRKO4SN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:35T5PG3MV7HJZEIRQQCSRKO4SN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9480c30fe4d08ec11a8dd93535833e2d80979b671974e75d4b62a5e2ec675b3e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-08-16T04:07:59Z","title_canon_sha256":"fee7af856cc5e9c3e7b61f92fd8fc2fef57e0ebd9bcbd45bce1dc4dd00714b32"},"schema_version":"1.0","source":{"id":"2108.06887","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.06887","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"arxiv_version","alias_value":"2108.06887v2","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.06887","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_12","alias_value":"35T5PG3MV7HJ","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_16","alias_value":"35T5PG3MV7HJZEIR","created_at":"2026-07-05T03:07:52Z"},{"alias_kind":"pith_short_8","alias_value":"35T5PG3M","created_at":"2026-07-05T03:07:52Z"}],"graph_snapshots":[{"event_id":"sha256:31a4ca75c3736641979c856a272fd227d3f80277f70fd72fe494f162fb9d4131","target":"graph","created_at":"2026-07-05T03:07:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2108.06887/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e.g., tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts ","authors_text":"Baocai Yin, Haiyin Piao, Jianchuan Ding, Lingping Gao, Wenxi Liu, Xin Yang, Yuxin Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-08-16T04:07:59Z","title":"A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.06887","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4a1ae51fa3a32995c97645a2b0fe5d126778f91e45b62cc64787259a2ed46b4c","target":"record","created_at":"2026-07-05T03:07:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9480c30fe4d08ec11a8dd93535833e2d80979b671974e75d4b62a5e2ec675b3e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2021-08-16T04:07:59Z","title_canon_sha256":"fee7af856cc5e9c3e7b61f92fd8fc2fef57e0ebd9bcbd45bce1dc4dd00714b32"},"schema_version":"1.0","source":{"id":"2108.06887","kind":"arxiv","version":2}},"canonical_sha256":"df67d79b6cafce9c9111840528a9dc937427a35b59630e30453ff389c37127c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df67d79b6cafce9c9111840528a9dc937427a35b59630e30453ff389c37127c2","first_computed_at":"2026-07-05T03:07:52.151717Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:07:52.151717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GOSXjkYkZF/cOFQ91z02hudT4GWzA8iP34Q1rmqyHI+gSO9ouVL6FOGSLGwBKeJ5e+qXjnWJft30mS4IwWkEAg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:07:52.152213Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.06887","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a1ae51fa3a32995c97645a2b0fe5d126778f91e45b62cc64787259a2ed46b4c","sha256:31a4ca75c3736641979c856a272fd227d3f80277f70fd72fe494f162fb9d4131"],"state_sha256":"480e3c278be16c67c5126e978dda0bf29ae18fda64ae203f3c91986f4a664b63"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DA9Fo1EvfNPyLwt8ujf9TUMDcu+9Sm3dFoMpRGqHww2wplLV968G+ZV2EiEfAlLpw45pQ358eWHxorTqJx+LCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T01:23:50.934814Z","bundle_sha256":"39377f932f39d14fca64f2bb523790f5de8754b8c2c4d2ede5a1f9c3d9d7dda4"}}