[Task Clean-up][Manager] Dexterous Part 6/8: Add the handover and camera manager counterparts

[hujc7]

Review Map

• Exact changes: a stacked PR's page shows the cumulative diff of its dependency chain; the link pins the commit range that is the PR's own contribution.
• Links pin specific SHAs and can go stale after a branch update — the table on [DO-NOT-MERGE][Task Clean-up] Dexterous: lumped validation branch (series reference) #6324 is refreshed first.

PR	Status	Depends on	Exact changes
#6410 [Docs] Environment overview regen		—	—
#6411 Part 1/8: Newton cloner/cubric/visualizer fixes		—	—
#6412 Part 2/8: OVPhysX articulation + manager runtime		—	—
#6413 Part 3/8: Reorient Direct, torch		—	—
#6414 Part 4/8: MARL-to-single-agent fix + handover/camera Direct		#6413	changes
#6418 Part 5/8: Reorient manager counterparts		#6413	changes
📌 #6421 Part 6/8: Handover + camera manager counterparts (this PR)		#6413, #6414, #6418	changes
#6415 Part 7/8: Benchmark success-rate utilities + docs		#6413, #6414, #6418, #6421	changes
#6582 Part 8/8: Warp variants → experimental (draft; merges last)		#6413	changes
#6324 [DO-NOT-MERGE] Lumped validation reference		ALL	—

Summary

• Adds manager-based counterparts for the Shadow handover and Shadow camera reorientation tasks, completing manager coverage of the dexterous families — consolidating the scope formerly tracked in [Task Clean-up][Manager][Camera] Dexterous Part 10/11: Add the Shadow camera manager counterpart #6420.
• Adds Direct-vs-manager scalar value-parity checks across all dexterous families.

Stacking

• Diamond join: needs both [Task Clean-up] Dexterous Part 4/8: Fix MARL-to-single-agent training and enable handover Direct RSL-RL #6414 and [Task Clean-up][Manager] Dexterous Part 5/8: Add the reorientation manager counterparts #6418 (branch contains their merge; own diff: Exact-changes link above).

Validation

• Kit-free suite green (30/30 incl. the parity tests). Full evidence: [DO-NOT-MERGE][Task Clean-up] Dexterous: lumped validation branch (series reference) #6324.

[greptile-apps]

Greptile Summary

This PR adds the manager-based counterpart for the two-hand Shadow Hand handover task (Isaac-Shadow-Handover), completing Part 11 of the dexterous task clean-up series. It introduces a new MDP term set (commands, events, observations, rewards, terminations), wires them into a HandoverManagerEnvCfg, and lands the family-wide OVPhysX acceptance test and handover configuration tests that depend on all prior parts being merged.

• The new manager env faithfully mirrors the Direct MARL adapter: scene/physics configs are reused from HandoverEnvCfg via a module-level _DIRECT_CFG sentinel, reward scaling compensates for the RewardManager's step_dt multiplication, and the _handover_reset_actions side-channel threads pre-reset actions into observations at episode boundaries.
• The mdp/__init__.py uses lazy_export() with a .pyi stub that declares from isaaclab.envs.mdp import *; this correctly sets up a lazy fallback for base MDP terms like EMAJointPositionToLimitsActionCfg, consistent with the reorient task's established pattern.
• CI is expected to stay red until the parent PRs ([Task Clean-up] Dexterous Part 3/8: Add success-rate metrics to the reorientation Direct tasks #6413, [Task Clean-up] Dexterous Part 4/8: Fix MARL-to-single-agent training and enable handover Direct RSL-RL #6414, [Task Clean-up][Manager] Dexterous Part 5/8: Add the reorientation manager counterparts #6418, [Task Clean-up][OVPhysX] Dexterous Part 7/11: Add OVPhysX presets to the dexterous tasks #6417, [Task Clean-up][Manager] Dexterous Part 9/11: Add Shadow state and OpenAI manager counterparts #6419, [Task Clean-up][Manager][Camera] Dexterous Part 10/11: Add the Shadow camera manager counterpart #6420) merge.

Confidence Score: 4/5

The change adds new files only (plus a two-line extension to init.py); no existing behavior is modified. The new env config correctly delegates scene/physics to the Direct base, and the MDP terms are consistent in frame conventions.

All newly added files follow the established lazy_export + PresetCfg pattern from the reorient task family. Frame conventions (local vs world) are consistent across commands, events, observations, and rewards. The single finding — storing two log values as zero-dim tensors while adjacent keys use .item() — is an inconsistency that could silently break a strict logging backend but does not affect training correctness.

source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/rewards.py — the dist_reward/dist_goal logging inconsistency noted above.

Important Files Changed

Filename	Overview
source/isaaclab_tasks/isaaclab_tasks/core/handover/handover_manager_env_cfg.py	New manager-based handover env config; delegates all scene/physics/action settings to the Direct base config via module-level _DIRECT_CFG and PresetCfg presets. Clean structure with no logic bugs found.
source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/rewards.py	HandoverReward class computes sticky episode success and logs diagnostics; dist_reward and dist_goal are stored as zero-dim tensors without .item(), unlike the Metrics/* keys. Reward scaling via /step_dt is intentional and explained in comments.
source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/commands.py	HandoverCommand samples fixed goal position from object default_root_pose plus offset and randomizes X/Y orientation per episode; consistent with Direct env's distribution. Goal position is correctly in local/env frame throughout.
source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/events.py	reset_handover_state mirrors Direct env's reset distribution for object pose/velocity and both hands; uses env._handover_reset_actions side-channel to thread pre-reset actions into observations cleanly.
source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/observations.py	All seven observation terms are frame-consistent (local/env frame for positions, world frame for velocities which are origin-invariant). object_goal returns a 24-dim block matching the Direct env layout.
source/isaaclab_tasks/isaaclab_tasks/core/handover/mdp/terminations.py	Two simple termination terms: height-based fall detection and direct_timeout that exactly matches the Direct MARL boundary (>= max_episode_length - 1).
source/isaaclab_tasks/isaaclab_tasks/core/handover/init.py	Registers new Isaac-Shadow-Handover manager env and adds rsl_rl_cfg_entry_point to the existing Direct registration.
source/isaaclab_tasks/test/core/test_handover_env_cfg.py	New config tests verify asset reuse, pose consistency across backends, and Newton distal-joint override. Tests look correct and cover the key structural invariants.
source/isaaclab_tasks/test/core/test_dexterous_ovphysx_presets.py	Family-wide OVPhysX acceptance test covering 15 dexterous variants including the new HandoverManagerEnvCfg; also tests Newton presets for allegro/handover variants.
source/isaaclab_tasks/test/benchmarking/configs.yaml	Adds Isaac-Shadow-Handover benchmark row with same thresholds as the existing Direct variant (max_iterations=3000, reward>=1000, episode_length>=150).

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[ManagerBasedRLEnv\nIsaac-Shadow-Handover] --> B[HandoverManagerEnvCfg]

    B --> C[HandoverManagerSceneCfg\nPresetCfg: physx / newton_mjwarp / ovphysx]
    C --> C1[_HandoverManagerSceneCfg\nright_hand / left_hand / object]
    C1 --> C2[_DIRECT_CFG.right_robot_cfg\n_DIRECT_CFG.left_robot_cfg]

    B --> D[ActionsCfg\nEMAJointPositionToLimitsActionCfg\nright_hand + left_hand]
    B --> E[CommandsCfg\nHandoverCommand\nfixed pos + random X/Y orientation]
    B --> F[ObservationsCfg\nPolicyCfg: right 157-dim + left 157-dim]
    B --> G[EventCfg\nreset_handover_state]
    B --> H[RewardsCfg\nHandoverReward\n2x exp reward / step_dt]
    B --> I[TerminationsCfg\nobject_below_height + direct_timeout]

    G -->|env._handover_reset_actions| J[hand_action obs term]
    H -->|sticky success flag| K[Metrics/success_rate at reset]

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%%{init: {'theme': 'base', 'themeVariables': {"darkMode": true, "background": "#0d1117", "primaryColor": "#21262d", "primaryTextColor": "#e6edf3", "primaryBorderColor": "#8b949e", "lineColor": "#8b949e", "textColor": "#e6edf3", "edgeLabelBackground": "#161b22", "actorBkg": "#21262d", "actorBorder": "#8b949e", "actorTextColor": "#e6edf3", "actorLineColor": "#8b949e", "signalColor": "#8b949e", "signalTextColor": "#e6edf3", "noteBkgColor": "#373320", "noteBorderColor": "#d4a72c", "noteTextColor": "#f0e6c0", "labelBoxBkgColor": "#21262d", "labelBoxBorderColor": "#8b949e", "labelTextColor": "#e6edf3", "loopTextColor": "#e6edf3", "activationBkgColor": "#30363d", "activationBorderColor": "#8b949e"}}}%%
flowchart TD
    A[ManagerBasedRLEnv\nIsaac-Shadow-Handover] --> B[HandoverManagerEnvCfg]

    B --> C[HandoverManagerSceneCfg\nPresetCfg: physx / newton_mjwarp / ovphysx]
    C --> C1[_HandoverManagerSceneCfg\nright_hand / left_hand / object]
    C1 --> C2[_DIRECT_CFG.right_robot_cfg\n_DIRECT_CFG.left_robot_cfg]

    B --> D[ActionsCfg\nEMAJointPositionToLimitsActionCfg\nright_hand + left_hand]
    B --> E[CommandsCfg\nHandoverCommand\nfixed pos + random X/Y orientation]
    B --> F[ObservationsCfg\nPolicyCfg: right 157-dim + left 157-dim]
    B --> G[EventCfg\nreset_handover_state]
    B --> H[RewardsCfg\nHandoverReward\n2x exp reward / step_dt]
    B --> I[TerminationsCfg\nobject_below_height + direct_timeout]

    G -->|env._handover_reset_actions| J[hand_action obs term]
    H -->|sticky success flag| K[Metrics/success_rate at reset]

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[greptile-apps]

Mixed tensor/scalar types in extras log dict. dist_reward and dist_goal are stored as zero-dim PyTorch tensors (.mean() without .item()), while the Metrics/* keys on the very next lines use .item() to produce Python floats. A logging backend that serializes extras["log"] to JSON or passes values to a scalar summarizer will silently fail or produce unexpected output for those two keys.

Suggested change

	env.extras.setdefault("log", {})["dist_reward"] = per_agent_reward.mean()
	env.extras["log"]["dist_goal"] = goal_distance.mean()
	env.extras["log"]["Metrics/goal_distance"] = goal_distance.mean().item()
	env.extras.setdefault("log", {})["dist_reward"] = per_agent_reward.mean().item()
	env.extras["log"]["dist_goal"] = goal_distance.mean().item()
	env.extras["log"]["Metrics/goal_distance"] = goal_distance.mean().item()