Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning

Feng Chen; Pei Zhou; Lixuan He; Yunchao Yao; Li Sun; Shenghua Gao; Mingyu Ding; Kaifeng Zhang

cs.ROOct 19, 2026

Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning

Authors: Feng Chen, Pei Zhou, Lixuan He, Yunchao Yao, Li Sun, Shenghua Gao, Mingyu Ding, Kaifeng Zhang,

Organizations: HKU · Sharpa · Tsinghua University · Stanford University ·

Abstract

Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependent stages. We introduce Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning throughout such tasks. Agents must track task progress, satisfy intermediate constraints, and revise their plans using execution feedback as earlier actions shape subsequent decisions. The playground comprises 410 task environments spanning basic manipulation, sequential puzzles, and multi-stage household activities, with variations in planning depth, geometric constraints, and physical execution demands. We curate EP-100, a representative selection of 100 tasks across five suites and 16 task types, together with the EP-Easy and EP-Hard difficulty splits and the compact EP-20 selection. A shared protocol specifies observation and tool access, resource budgets, repeated trials, and independent checks of goal completion, task-rule compliance, and physical validity. Embodied Playground provides a unified framework for studying whether foundation-model agents can sustain effective reasoning and action over extended task horizons. https://winniechen2002.github.io/Agent-bench-website/

Figures & tables

Figure 2: Agent–environment interaction in Embodied Playground. The agent writes and executes code through the public SDK and revises its actions using environment feedback. Access levels provide cumulative capabilities. Launcher images show an illustrative reference-controller rollout.

Task family	Count	Task focus	Embodiments
Launcher	100	Aiming, indirect impacts, fracture, target constraints	Dual Panda
Tower of Hanoi	120	Legal rearrangement, grasping, peg insertion	Panda; FR3–Sharpa; floating Sharpa
Pocket cube	20	Prescribed layer turns and regrasping	ALOHA; dual floating Sharpa
Rubik’s cube	60	Cube solving and physical face turns	Panda; floating Sharpa
Kongming lock	60	Partial and full assembly and disassembly	Dual Panda; dual floating Sharpa
Bookshelf	20	Sorting, arrangement, and clutter removal	Dual Panda

Table 1: The full Embodied Playground collection. Counts denote task–embodiment combinations. The ManiSkill group contains ten tasks each for long-horizon manipulation, geometry, and dynamics.

Suite	Type	Tasks	E / H	Principal demand
Launch	Aim	7	4 / 3	Direct shots at single or paired targets, including azimuth aiming
	Causal	13	6 / 7	Indirect hits through rolling bodies, dominoes, and collapsing structures
	Glass	5	3 / 2	Breakable glass panes with fracture thresholds
	Rules	9	4 / 5	Ordered or protected targets and recovery after a miss
Tower	Plan	5	3 / 2	Planning depth from 1 to 31 moves on a standard board
	Physics	8	4 / 4	Physical board variations under the same legal-move rule

Table 2: EP-100 suites and task types. E and H give the number of tasks of each type in EP-Easy and EP-Hard.

Subset	Tasks	Launch	Tower	Lock	Cube	Home	Conditions
EP-100	100	34	21	16	12	17	400
EP-Easy	50	17	11	8	6	8	200
EP-Hard	50	17	10	8	6	9	200
EP-20	20	6	4	3	3	4	80

Table 3: EP-100 subsets. Counts give tasks per suite and registered conditions per fixed evaluation setting, with loaded-only launcher initialization. Each condition uses one attempt for Pass@1 and at most two for Pass@2. EP-Easy and EP-Hard partition EP-100, and EP-20 draws ten tasks from each.

Task family	Physics/control (Hz)	Maximum actions	Simulated time	Wall time
Launcher	1,000/50	7,500	150	600
Hanoi	120/30	18,000	600	3,600
Pocket cube	240/60	18,000	300	3,600
Rubik’s cube	120/30	109,800	3,660	19,800
Kongming lock	120/30	36,000	1,200	3,600
Bookshelf	120/30	1,500–12,300	50–410	3,600

Table 4: Per-episode execution budgets. Times are in seconds. The bookshelf limit depends on the task configuration. Task-specific event deadlines apply in addition to these episode limits.

Subset	Model	Tasks	Conditions	Attempts	Pass@1	Pass@2
EP-100	GPT-6-Astra	100	400	736	13.80	19.94
	Opus-5.5-extra	100	400	683	29.22	35.85
EP-Easy	GPT-6-Astra	50	200	358	18.42	27.41
	Opus-5.5-extra	50	200	332	33.99	42.88
EP-Hard	GPT-6-Astra	50	200	378	8.81	12.01
	Opus-5.5-extra	50	200	351	24.21	28.48

Table 5: GPT-6-Astra and Opus-5.5-extra results on the four EP subsets. Pass@1 and Pass@2 are suite-balanced percentages. Attempts count valid opportunities used. EP-Easy and EP-Hard partition EP-100, while EP-20 overlaps both and uses the same records. Launch is loaded-only throughout.

Suite	Model	EP-100	EP-Easy	EP-Hard	EP-20
Launch	GPT-6-Astra	19.12 / 25.74	20.59 / 30.88	17.65 / 20.59	12.50 / 16.67
	Opus-5.5-extra	15.51 / 27.70	15.21 / 32.60	15.81 / 22.81	5.65 / 19.78
Tower	GPT-6-Astra	29.76 / 41.67	45.45 / 61.36	12.50 / 20.00	12.50 / 25.00
	Opus-5.5-extra	60.59 / 66.14	71.51 / 79.82	48.58 / 51.09	50.00 / 56.25
Lock	GPT-6-Astra	6.25 / 14.06	12.50 / 28.13	0.00 / 0.00	0.00 / 0.00
	Opus-5.5-extra	39.81 / 45.63	52.03 / 60.56	27.60 / 30.70	37.63 / 37.63

Table 6: Suite-level success rates (%). Each cell gives Pass@1 / Pass@2. Conditions are averaged within tasks and tasks within suites. The subset scores in Table 5 average the five suite entries.

		GPT-6-Astra		Opus-5.5-extra
Suite	Cond.	Success	Failure	Success	Failure	Time@S (min)
Launch	136	35	101	36	100	6.2
Tower	84	35	49	55	29	38.5
Lock	64	9	55	20	44	37.0
Cube	48	1	47	5	43	32.5
Home	68	11	57	17	51	26.6

Table 7: EP-100 condition outcomes and Opus-5.5-extra successful-attempt timing. Success and failure count conditions after at most two valid attempts, rather than individual attempts. Time@S is the mean wall-clock time of successful attempts in minutes and excludes prior unsuccessful attempts.

Suite	Failed attempts	Frequent labels (count)
Launch	211	Limit (129), rule (58)
Tower	108	Penetration (35), displacement/loss (33), limit (27)
Lock	115	Contact (97), penetration (19)
Cube	94	Mechanism (64), limit (16)
Home	117	Time (40), penetration (27), displacement/loss (23)

Table 8: Frequent diagnostic labels among GPT-6-Astra scored failed attempts. Counts are incidences within each suite, not task success rates. Multiple labels may apply to one failure; only the most frequent labels are shown.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Tier	Tasks	Case IDs
1	4	A007, A008, A009, A010
2	9	A001, A011, A012, A013, A017, A018, A023, A037, A075
3	25	A002, A003, A004, A014, A019, A020, A024, A025, A026, A038, A039, A040, A044, A049, A050, A053, A054, A056, A059, A061, A064, A071, A076, A077, A080
4	40	A005, A006, A015, A016, A021, A022, A027, A028, A029, A030, A041, A042, A043, A045, A048, A051, A052, A055, A057, A058, A060, A062, A063, A065, A066, A069, A070, A072, A073, A078, A079, A081, A082, A083, A084, A085, A086, A090, A091, A094
5	22	A031, A032, A033, A034, A035, A036, A046, A047, A067, A068, A074, A087, A088, A089, A092, A093, A095, A096, A097, A098, A099, A100

Appendix

Table 9: Complete launcher index by native tier. Case numbers are not ordered by tier.

Cases	Variation	Principal demand
H01–H04	State decomposition	Identify the required subproblem and maintain legal ring ordering.
H05–H08	Planar layout	Adapt transfers to different peg arrangements.
H09–H12	Support height	Transfer rings between supports at different elevations.
H13–H16	Insertion clearance	Align ring holes and pegs under varying clearance.
H17–H20	Mass distribution	Adapt lifting and transport to ring masses.
H21–H24	Friction	Maintain stable grasps under different contact conditions.

Appendix

Table 10: Hanoi configuration index. Each row contributes four cases per embodiment and twelve environments in total.

Case	Level	Transition	Manipulation demand
C01	1	U	Support the lower layer during a top-layer turn.
C03	1	L	Create clearance for a left-layer turn.
C07	2	D	Free the bottom layer from its support.
C08	2	U2	Complete a half turn through intermediate detents.
C09	3	L	Control a side-layer turn with increased mass.
C10	3	R’	Maintain contact during a low-friction reverse turn.

Appendix

Table 11: Pocket-cube case index. Standard face notation is used; a prime denotes an inverse turn and 2 denotes a half turn.

Cases	Native level	Scramble tokens	Environments
C01–C10	Easy	5	20
C11–C20	Medium	10	20
C21–C30	Hard	15	20

Appendix

Table 12: Rubik’s cube index. Each case range appears under both embodiment prefixes.

Assembly	Disassembly	Objective	Environments
A01–A05	D01–D05	Complete six-piece lock	20
A06–A10	D06–D10	Key only	20
A11–A15	D11–D15	Key and one bar	20

Appendix

Table 13: Complete lock index. Every listed range is instantiated with both embodiments.

Case	Task configuration
BooksTableD1	Three upright books onto the middle shelf
BooksTableD2	Five books, some lying flat
BooksTableD3	Books onto the middle shelf
BooksTableD4	Ten books against a named wall
ShelfTableD1	Books, a trophy and one box
ShelfTableD2	Books, three display objects and two boxes

Appendix

Table 14: Bookshelf case index. All IDs have the prefix bookshelf/dual-panda/ .

Case	Robot identifier	Objective
L1	fetch	Clear a writing area, store books and stationery, close the drawer, and preserve a colleague’s objects.
L2	everyday_robot_09	Store ingredients and utensils, retract the oven rack, switch off both knobs, and close storage without disturbing the loaf.
L3	fetch	Arrange two mug–spoon settings, clear the serving area, close storage, and preserve the third setting.
L4	everyday_robot_03	Select the specified screwdriver, handle, and screws; pack, close, and latch the repair case.
L5	fetch	Unload specified mugs and cutlery, preserve the bowl, retract the rack, and close the dishwasher and storage.
L6	fetch	Pack bottles below a divider and food above it; preserve the separate lunch and close storage.

Appendix

Table 15: Long-horizon household task index.

Case	Robot identifier	Objective
P1	panda	Seat round, square, and long pegs in their matching sockets without moving the board.
P2	everyday_robot_09	Insert a key, turn it clockwise by a quarter turn, and withdraw it onto the mat.
P3	panda	Align and seat a keyed connector while preserving neighboring connectors.
P4	everyday_robot_03	Align an asymmetric lid with its corner tab and seat the entire rim.
P5	panda	Guide a hook through a mug handle and release it in stable suspension.
P6	everyday_robot_09	Extract a spatula through a cluttered holder without disturbing adjacent utensils.

Appendix

Table 16: Geometric precision task index.

Case	Robot identifier	Objective
D1	panda	Stop a rolling orange on a safe mat before it leaves the counter.
D2	panda	Position a catcher beneath a ball leaving a ramp and retain it after impact.
D3	panda	Push a puck once so that it coasts to rest inside the scoring region.
D4	everyday_robot_01	Transport a loaded tray while keeping cups upright and contents contained.
D5	panda	Push a tall bottle around a guide block without tipping it.
D6	panda	Close a loaded drawer within five seconds without disturbing its contents.

Appendix

Table 17: Dynamic interaction task index.

Suite	Workload score
Launch	Ten times the native tier, plus the loaded-mode difficulty level D1–D5 recorded in the task atlas, plus the number of required targets
Tower	Twice the depth index, which is 0 to 3 for 1, 3, 9, and 31 moves, plus 1 for a physical theme, plus 2 for FR3–Sharpa or 3 for floating Sharpa
Lock	Subtask weight of 0 for the key, 2 for the key and bar, and 4 for the full lock, plus 1 for a hard pose, plus 0.5 for assembly, plus 2 for two floating Sharpa hands
Cube	Scramble or transition length in quarter turns, plus 3 for a Sharpa embodiment
Home	Number of objects for bookshelf tasks, and number of ordered milestones and final predicates for ManiSkill tasks

Appendix

Table 18: Workload scores that rank tasks within each type. Higher scores are harder. Ties are broken by embodiment, with parallel grippers first, and then by task identifier.

Code	Meaning
COLL	Forbidden or excessive contact (robot-object, arm-arm, force limit)
PEN	Penetration beyond the audited limit
LIMIT	Joint, speed or physics limit exceeded
DROP	Object dropped, unseated or pushed out of the workspace
RULE	Task rule broken (illegal move, wrong order, protected item hit, launcher interlock)
MECH	Assist-mechanism constraint broken (cube fixture relation, mechanism command)

Appendix

Table 19: Failure categories. Each failed trial receives the category of its first recorded violation or of its termination reason.

Index	Type	Task	Split	EP-20	Environment identifier
Launch-01	Aim	Two-range ammunition planning	Hard		marble/dual-panda/A001
Launch-02	Aim	Single-target warm-up	Easy	✓	marble/dual-panda/A007
Launch-03	Aim	Long-range energy budget	Easy		marble/dual-panda/A008
Launch-04	Aim	Elevated-platform shot	Easy		marble/dual-panda/A009
Launch-05	Aim	Toppling timber gate	Easy		marble/dual-panda/A010
Launch-06	Aim	Three-target fan	Hard		marble/dual-panda/A075

Appendix

Table 20: Complete EP-100 index.

Type	Tasks	Index numbers
Launch-Aim	4	02, 03, 04, 05
Launch-Causal	6	08, 09, 10, 11, 12, 15
Launch-Glass	3	21, 22, 23
Launch-Rules	4	26, 27, 28, 32
Tower-Plan	3	01, 02, 03
Tower-Physics	4	06, 07, 09, 11

Appendix

Table 21: EP-Easy index by task type.

Type	Tasks	Index numbers
Launch-Aim	3	01, 06, 07
Launch-Causal	7	13, 14, 16, 17, 18, 19, 20
Launch-Glass	2	24, 25
Launch-Rules	5	29, 30, 31, 33, 34
Tower-Plan	2	04, 05
Tower-Physics	4	08, 10, 12, 13

Appendix

Table 22: EP-Hard index by task type.

Index	Task	Type	Split	Embodiment	Native level
Launch-02	Single-target warm-up	Aim	Easy	Dual Panda	tier 1
Launch-08	Orchard rolling bodies	Causal	Easy	Dual Panda	tier 2
Launch-22	Two glass layers	Glass	Easy	Dual Panda	tier 3
Launch-19	Movable flap at a narrow throat	Causal	Hard	Dual Panda	tier 4
Launch-31	Fragment-risk well	Rules	Hard	Dual Panda	tier 5
Launch-34	Material-ordered courtyard	Rules	Hard	Dual Panda	tier 5

Appendix

Table 23: EP-20 index.

Embodied Agent Benchmarks

See all 40 papers

MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures
Oct 9, 2026Loan Bernat, Matthieu Grard, Ariane Herbulot +1Embodied Agent Benchmarks Long-Horizon Agent Evaluation
LAAS-GEPETTO · LAAS-RAP
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Apr 28, 2026Yixuan Huang, Bowen Li, Vaibhav Saxena +9Robot Task Planning Robot Learning
Princeton University · Carnegie Mellon University · Georgia Tech +3
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Oct 20, 2025Yulin Luo, Chun-Kai Fan, Menghang Dong +19Long-Horizon Robotic Manipulation Multimodal Large Language Models
State Key Laboratory of Multimedia Information Processing, School of Computer2026 Science, Peking University · Institute for Brain and Intelligence, Fudan University · University of Science andJul Technology Beijing +2

Oct 9, 2026J/K move · Enter open · S save

MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures

Loan Bernat, Matthieu Grard, Ariane Herbulot, Florent Lamiraux

LAAS-GEPETTO · LAAS-RAP

Embodied Agent Benchmarks Long-Horizon Agent Evaluation Long-Horizon Agent Tasks Embodied AI Multi-Robot Coordination Multi-Robot Motion Planning

Open paper AskPDF arXiv

Long-horizon embodied agents must maintain task-relevant information, coordinate heterogeneous robots, and adapt their decisions as execution unfolds. Existing benchmarks investigate dynamic task execution and embodied multi-agent coordination, but provide limited evaluation of their intersection. We introduce MAGMA-Bench, an architecture-agnostic benchmark for persistent task reasoning in dynamic multi-robot environments. MAGMA-Bench evaluates agents across increasing task horizons and controlled online perturbations, including execution failures, task interruptions, and mission updates, with scenarios requiring coordination across robots with heterogeneous capabilities. It uses a structured skill-level interface to separate task-level reasoning from perception and low-level control. We evaluate locally deployable open-weight planners, multi-agent state-tracking strategies, and frontier models. Even the strongest evaluated models fail on a substantial fraction of clean episodes, and their success rates decrease further under online perturbations. Supervised adaptation greatly improves a small model, but performance remains substantially lower on longer and multi-robot missions. We release the benchmark, 261,646 task-level training examples, and an architecture-agnostic expert-trace generation pipeline.

Abstract

Embodied Agent Benchmarks

Explore similar work

MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures

KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning

Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain