[Task Clean-up][Manager] Dexterous Part 6/10: 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 — this table on [DO-NOT-MERGE][Task Clean-up] Dexterous: lumped validation branch (series reference) #6324 is refreshed first.

PR	Status	Depends on	Exact changes
#6411 Part 1/10: Newton cloner/cubric/visualizer fixes		—	merged
#6412 Part 2/10: OVPhysX articulation + manager runtime		—	merged
#6413 Part 3/10: Reorient Direct, torch		—	changes
#6414 Part 4/10: MARL-to-single-agent fix + handover/camera Direct		#6413	changes
#6418 Part 5/10: Reorient manager counterparts		#6413	changes
📌 #6421 Part 6/10: Handover + camera manager counterparts		#6413, #6414, #6418	changes
#6410 Part 7/10: Environment overview docs		#6421	changes
#6954 Part 8/10: OpenAI Shadow variants → contrib (draft)		#6421	changes
#6415 Part 9/10: Benchmark success-rate utilities		#6421	changes
#6582 Part 10/10: Warp variants → experimental (draft; merges last)		#6413	changes
#6324 [DO-NOT-MERGE] Lumped validation reference		ALL	—

Summary

Completes manager coverage of the dexterous task families by adding manager-based counterparts
for the Shadow handover and Shadow camera reorientation tasks. Stacks on
#6418 — [Task Clean-up][Manager] Dexterous Part 5/9: Add the reorientation manager counterparts

• 2 manager tasks added: Isaac-Handover-Shadow and Isaac-Reorient-Cube-Shadow-Camera.
• The last 2 -Play registrations in isaaclab_tasks are removed.

1. Handover manager counterpart

The fused handover reward became a plain reward term, with success and goal-distance
bookkeeping moved to HandoverCommand, which owns the goal. It reports Metrics/success_rate as
a per-episode bit, matching the handover Direct environment. A value-parity test covers the
handover pair alongside the reorientation ones.

HandoverCommand.command builds its pose per call rather than returning a persistent buffer, so
consumers that store it across steps are not aliased to live data.

2. Camera manager counterpart

The camera task runs on PhysX by default: the RTX render modalities require Fabric cloning,
which Newton does not support, so the inherited default could not render. Newton remains
selectable with physics=newton_mjwarp for the state-only observation groups.

The feature-extractor observation term ignored its declared feature_extractor_cfg parameter
and read the environment configuration instead; it now honours the parameter.

3. Camera playback moves to play_mode

Isaac-Reorient-Cube-Shadow-Camera-Play and -Camera-Direct-Play were the last -Play
registrations left in isaaclab_tasks; every other task moved to play_mode in
#6676 — Remove the _PLAY configs. Their playback
environment count and inference feature extractor now live in play_mode overrides on the
manager and Direct camera configurations.

The override mutates the feature extractor rather than replacing it. Replacing the object
resets every field the caller does not name, which silently re-enabled the CNN in
ShadowHandCameraBenchmarkEnvCfg — a configuration whose entire purpose is to disable it.

4. Reorientation action configuration

The action term is named through a module path, so loading a task configuration no longer
imports the USD bindings.

Validation

Handover manager reaches a success rate of 0.859 against the Direct task's 0.667 over 1500
iterations at seed 42. The camera task's registered-rendering matrix and golden images are
tracked separately.

[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 3/9: Add success-rate metrics to the reorientation Direct tasks #6413, [Task Clean-up] Dexterous Part 4/9: Fix MARL-to-single-agent training and enable handover Direct RSL-RL #6414, [Task Clean-up][Manager] Dexterous Part 5/10: 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()